Asta Literature Retrieval: Pathophysiology and clinical mechanisms of 3-hydroxyisobutyryl-CoA hydrolase deficiency. Core disease mechanisms, mol...

This report is retrieval-only and is generated directly from Asta results.

• Papers retrieved: 20
• Snippets retrieved: 20

Relevant Papers

[1] Changes in Serum Proteomic Profiles at Different Stages of Pregnancy Toxemia in Goats

• Authors: M. Uzti̇mür, C. N. Ünal, Gurler Akpinar
• Year: 2025
• Venue: Journal of Veterinary Internal Medicine
• URL: https://www.semanticscholar.org/paper/4b9c488b5dbd65d7b26fd2ad9aed70e8c4b59942
• DOI: 10.1111/jvim.70139
• PMID: 40492724
• PMCID: 12150350
• Summary: Understanding the serum proteome profiles of goats with pregnancy toxemia might help identify the proteomes and pathways responsible for the development of this disease and improve diagnosis and treatment.
• Evidence snippets:
• Snippet 1 (score: 0.486) > The pathophysiology and progression of this disease are not fully understood. > Traditional biomedical research has focused on the analysis of single genes, proteins, metabolites, or metabolic pathways in diseases. This molecular reductionist approach is based on the assumption that identifying genetic variations and molecular components will lead to new treatments for diseases [13][14][15][16]. However, many diseases are complex and multifactorial, and in order to determine the phenotype of such diseases, it is necessary to understand the changes that occur in more than one gene, pathway, protein, or metabolite at the cellular, tissue, and organismal levels [17][18][19]. Therefore, in recent years, proteomics, as one field of multi-omics technologies, has helped in evaluating the complex pathogenetic mechanisms of different diseases from a broad perspective and has made substantial contributions [20,21]. In veterinary medicine, proteomic analysis of metabolic diseases such as ketosis [16], hypocalcemia [22], and fatty liver [23] in dairy cows has contributed valuable insights for the definition of new pathophysiological pathways and new diagnosis and treatment protocols for these diseases. The proteomic approach can contribute importantly to a broad and detailed understanding of the changes that occur at the organismal level associated with the increase in BHBA concentration in goats with pregnancy toxemia. Our aim was to evaluate the serum protein profiles of goats with SPT or CPT using proteomic techniques to determine the proteomic profiles of these animals and to identify the relevant pathophysiological mechanisms.

[2] Pediatric Paroxysmal Exercise-Induced Neurological Symptoms: Clinical Spectrum and Diagnostic Algorithm

• Authors: F. R. Danti, F. Invernizzi, I. Moroni, B. Garavaglia, N. Nardocci et al.
• Year: 2021
• Venue: Frontiers in Neurology
• URL: https://www.semanticscholar.org/paper/92b36a8a32d63b0a6cb99345cd885e3a6018171c
• DOI: 10.3389/fneur.2021.658178
• PMID: 34140924
• PMCID: 8203909
• Citations: 4
• Summary: The clinical, genetic, pathophysiologic, and therapeutic landscape of paroxysmal exercise induced neurological symptoms is reviewed, focusing on phenomenology and differential diagnosis.
• Evidence snippets:
• Snippet 1 (score: 0.482) > PED has been recently associated with the deficiency of a number of mitochondrial enzymes involved in energy production and branched-chain amino acids (BCAA; leucine, isoleucine, and valine) catabolism. They include Pyruvate dehydrogenase (PDH) complex, short-chain enoyl-CoA hydratase (ECSH1) and 3-Hydroxyisobutyryl-CoA hydrolase (HIBCH) (Figure 2) (33, 34). > PDH complex catalyses the oxidative decarboxylation of pyruvate with the production of acetyl-CoA; therefore, it connects the glycolytic pathway to the Krebs cycle and plays a central role in glucose metabolism in fed and fasting states. PDH complex is composed of three catalytic subunits: pyruvate dehydrogenase (PDH; E1, a heterotetramer of 2 subunits encoded by PDHA1 and PDHB1 genes), dihydrolipoamide acetyltransferase (E2, encoded by DLAT gene), and dihydrolipoamide dehydrogenase (E3, encoded by DLD gene), and of an additional component, the E3-binding protein (encoded by PDHX1) (35, 36). PDHA1, DLAT, and PDHX1 mutations have been linked to continuously expanding phenotypes inherited with X-linked (PDHA1 mutations, representing the main cause of PDH deficiency) or autosomal recessive pattern (DLAT and PDHX1 mutation). Clinical findings range from severe infantile lactic acidosis to milder chronic neurological disorders including intermittent and recurrent acute neurological symptoms such as episodic ataxia, peripheral weakness, and movement disorders such as PED and PNKD (36-38). In few patients recurrent dystonic or hemidystonic attacks have been described; they are triggered by prolonged walking and running and occur as a unique clinical manifestation or within complex neurological phenotypes (39-41).

[3] Cinical, Metabolic, and Genetic Analysis and Follow-Up of Eight Patients With HIBCH Mutations Presenting With Leigh/Leigh-Like Syndrome

• Authors: Junling Wang, Zhimei Liu, Manting Xu, Xiaodi Han, C. Ren et al.
• Year: 2021
• Venue: Frontiers in Pharmacology
• URL: https://www.semanticscholar.org/paper/d344b1c2d00932f15f1b993fbd15d7852ed52b06
• DOI: 10.3389/fphar.2021.605803
• PMID: 33762937
• PMCID: 7982470
• Citations: 14
• Influential citations: 3
• Summary: The purpose of this study was to analyze the phenotypic spectrum, follow-up results, metabolites, and genotypes of patients with HIBCH deficiency presenting with Leigh/Leigh-like syndrome and explore specific metabolites related to disease diagnosis and prognosis through retrospective and longitudinal studies.
• Evidence snippets:
• Snippet 1 (score: 0.472) > 3-Hydroxyisobutyryl-CoA hydrolase (HIBCH, NM_014362.3) gene mutation can cause HIBCH deficiency, leading to Leigh/Leigh-like disease. To date, few case series have investigated the relationship between metabolites and clinical phenotypes or the effects of treatment, although 34 patients with HIBCH mutations from 27 families have been reported. The purpose of this study was to analyze the phenotypic spectrum, follow-up results, metabolites, and genotypes of patients with HIBCH deficiency presenting with Leigh/Leigh-like syndrome and explore specific metabolites related to disease diagnosis and prognosis through retrospective and longitudinal studies. Applying next-generation sequencing, we identified eight patients with HIBCH mutations from our cohort of 181 cases of genetically diagnosed Leigh/Leigh-like syndrome. Six novel HIBCH mutations were identified: c.977T>G [p.Leu326Arg], c.1036G>T [p.Val346Phe], c.750+1G>A, c.810-2A>C, c.469C>T [p.Arg157], and c.236delC [p.Pro79Leufs5]. The Newcastle Pediatric Mitochondrial Disease Scale (NPMDS) was employed to assess disease progression and clinical outcomes. The non-invasive approach of metabolite analysis showed that levels of some were associated with clinical phenotype severity. Five (5/7) patients presented with elevated C4-OH in dried blood spots, and the level was probably correlated with the NPMDS scores during the peak disease phase. 2,3-Dihydroxy-2-methylbutyrate in urine was elevated in six (6/7) patients and elevated S-(2-caboxypropyl)cysteamine in urine was found in three patients (3/3). The median age at initial presentation was 13 months (8–18 months), and the median follow-up was 2.3 years (range 1.3–7.2 years). We summarized and compared with all reported patients with HIBCH mutations. The most prominent clinical manifestations were developmental regression/

[4] Identification of HIBCH and MGME1 as Mitochondrial Dynamics‐Related Biomarkers in Alzheimer's Disease Via Integrated Bioinformatics Analysis

• Authors: Hailong Li, Fei Feng, Shou-pin Xie, Yanping Ma, Yafeng Wang et al.
• Year: 2025
• Venue: IET Systems Biology
• URL: https://www.semanticscholar.org/paper/77185e17e1f3375b9031c9851afadaced3eecc7a
• DOI: 10.1049/syb2.70018
• PMID: 40286336
• PMCID: 12033025
• Citations: 2
• Summary: HIBCH and MGME1 are promising diagnostic biomarkers for AD with AUC values of 0.73 and 0.74 and Mechanistically, miR‐922 was experimentally validated to directly bind MGME1 3′UTR.
• Evidence snippets:
• Snippet 1 (score: 0.471) > In summary, MGME1 deficiency may impair mtDNA repair, leading to mitochondrial genome instability and bioenergetic failure. These findings collectively nominate MGME1 as a promising AD biomarker, with therapeutic targeting of MGME1 potentially mitigating pathological mtDNA-mediated neuroinflammation. The 3-hydroxyisobutyryl-coenzyme A (CoA) hydrolase (HIBCH) enzyme, which is encoded by HIBCH, is involved in significant stages of valine degradation. Neurological symptoms resulting from uncommon metabolic dysfunctions are caused by HIBCH deficit (HIBCHD) [27]. An uncommon condition of the mitochondrial valine metabolism known as 3-Hydroxyisobutyryl-CoA hydrolase (HIBCH) deficiency can cause organic aciduria, motor delay, hypotonia, ataxia, dystonia, seizures, poor eating and developmental regression or delay [4]. Both HIBCH deficiency and Leigh/ Leigh-like illness are caused by mutations in the 3-Hydroxyisobutyryl-CoA hydrolase (HIBCH) gene [4]. Leighlike disease and HIBCH deficiency can be caused by mutations in the 3-Hydroxyisobutyryl-CoA hydrolase (HIBCH) gene [4]. Neuroimaging results show abnormalities in signals in the cerebral peduncles and globus pallidi, which are located in the deep grey matter [4]. The findings of neuroimaging reveal anomalies in signals within the deep grey matter, specifically in the cerebral peduncles and globus pallidi [28,29]. It was discovered that HIBCH is one of the proteins that is overexpressed in malignancies, such as ovarian tumours, and that is expressed differentially in mitochondria [30], Blocking 3hydroxyisobutyryl-CoA hydrolase (HIBCH) to stop the breakdown of valine resulted in decreased intracellular succinate, a decrease in the development of cancerous prostate cells, and impaired cellular respiration [31].

[5] HIBCH mutations can cause Leigh-like disease with combined deficiency of multiple mitochondrial respiratory chain enzymes and pyruvate dehydrogenase

• Authors: S. Ferdinandusse, H. Waterham, S. Heales, Garry K. Brown, I. Hargreaves et al.
• Year: 2013
• Venue: Orphanet Journal of Rare Diseases
• URL: https://www.semanticscholar.org/paper/54d1e94c64b6353cdcf9e97e451e68839e044cd0
• DOI: 10.1186/1750-1172-8-188
• PMID: 24299452
• PMCID: 4222069
• Citations: 80
• Influential citations: 7
• Summary: HIBCH deficiency, a disorder of valine catabolism, is a novel cause of the multiple mitochondrial dysfunctions syndrome, and should be considered in the differential diagnosis of patients presenting with multiple RC deficiencies and/or pyruvate dehydrogenase deficiency.
• Evidence snippets:
• Snippet 1 (score: 0.470) > Mitochondrial disorders affect approximately 1 in 5000 births, and are clinically, biochemically and genetically heterogeneous [1]. Combined deficiency of multiple respiratory chain (RC) enzymes is one of the most frequent findings in children with suspected mitochondrial disease, representing approximately 30% of cases in whom a biochemical abnormality is identified. Approximately 50% of patients with multiple RC deficiencies have impaired replication or maintenance of the mitochondrial DNA (mtDNA), leading to progressive depletion of mtDNA [2] or accumulation of multiple mtDNA deletions. The remaining~50% of cases have heterogeneous underlying causes, including mitochondrial or nuclear-encoded defects of mitochondrial protein synthesis [3] and the multiple mitochondrial dysfunctions syndrome, in which the activity of PDHc is also impaired [4][5][6]. Defects in mtDNA repair, maintenance or translation result in combined deficiency of complexes I, III and IV (i.e. complexes that contain mtDNA-encoded subunits) whereas the multiple mitochondrial dysfunctions syndrome usually affects complexes containing iron-sulphur (Fe-S) clusters (complexes I, II and III) as well as PDHc. > Neurological features of cerebral organic acidurias (disorders of degradation of the carbon skeleton of amino acids) can be clinically and radiologically indistinguishable from mitochondrial encephalomyopathies caused by primary RC deficiencies; seizures, neurological regression and bilateral symmetrical basal ganglia lesions may occur in both groups of disorders [7][8][9][10]. 3-Hydroxyisobutyryl-CoA hydrolase (HIBCH) is a mitochondrial enzyme that catalyses the fifth step of valine catabolism, the conversion of 3-hydroxy-isobutyryl-CoA to 3-hydroxyisobutyrate ( Figure 1a). HIBCH deficiency has previously been reported in only two patients [11,12]. We now describe two new genetically confirmed cases (siblings), one of whom presented with combined defects of multiple RC enzymes and the pyruvate dehydrogenase complex (PDHc). This potentially represents a new disease mechanism mimicking the multiple mitochondrial dysfunctions syndrome, namely degradation of multiple enzymes resulting from accumulation of a toxic metabolite methacrylyl

[6] Current and Emerging Issues in Familial Hypobetalipoproteinemia-related Steatotic Liver Diseases

• Authors: Tian-Wen Lou, Tian-Yi Ren, Jian-gao Fan
• Year: 2025
• Venue: Journal of Clinical and Translational Hepatology
• URL: https://www.semanticscholar.org/paper/cf1c6534ba154bbb870b421a4e111acb62023405
• DOI: 10.14218/JCTH.2025.00360
• PMID: 41473260
• PMCID: 12745358
• Summary: Challenges include insufficient diagnosis, sparse epidemiological data, and unclear disease progression; enhanced genetic testing, mechanistic research, and longitudinal studies are critical to improving diagnosis, risk assessment, and therapies for FHBL-associated liver disease.
• Evidence snippets:
• Snippet 1 (score: 0.454) > ecent advances in molecular genetics have refined our understanding of APOB-related pathophysiology, yet critical questions remain unanswered, particularly regarding why some FHBL patients develop progressive liver disease while others remain stable, and what the specific mechanisms and molecular pathways are for the occurrence and development of liver disease. > This review aims to refocus attention on the hepatic aspects of FHBL by (i) summarizing current knowledge of molecular genetics, (ii) analyzing the spectrum and progression of liver disease in affected individuals, (iii) exploring mechanistic hypotheses that may explain liver disease, and (iv) outlining the clinical treatment plan. We aim to establish FHBL not only as a disorder of lipid metabolism but also as a valuable window into the pathogenesis of steatotic liver disease.

[7] HMG–CoA Lyase Deficiency

• Authors: B. Puisac, María Arnedo, M. Gil-Rodríguez, E. Teresa, Á. Pié et al.
• Year: 2011
• Venue: Unknown venue
• URL: https://www.semanticscholar.org/paper/ffd0bf7b7af0c6b88ada7c59a737d4d83e10c3b2
• DOI: 10.5772/20252
• Citations: 4
• Summary: A recent study of differential expression of human HL in liver, pancreas, testis, heart, skeletal muscle and brain that can help us to understand the consequences of this deficiency and draw a map of incidence.
• Evidence snippets:
• Snippet 1 (score: 0.449) > The HMG-CoA lyase (HL) deficiency or 3-hydroxy-3-methylglutaric aciduria (MIM 246450) is an inborn error of intermediary metabolism that was first described in 1976 by Faull et al (Faull et al., 1976). Because its clinical manifestations, it has been included within the Sudden Infant Death Syndrome (Wilson et al., 1984). At present, it is considered a rare disease (<1/100,000 live neonates) that should be diagnosed at early age because there is a simple and effective treatment (Watson et al., 2006). HL is a mitochondrial enzyme that catalyzes the cleavage of HMG-CoA to acetyl-CoA and acetoacetate, which is the common final step of ketogenesis and leucine catabolism (Figure 1). Patients with this disease suffer on the one hand, the absence of ketone bodies as alternative energy source of glucose and on the other hand, the accumulation of toxic metabolites of leucine catabolism. The most frequently affected organs are the liver and the brain, but the pancreas and the heart can also be involved. This chapter discusses a recent study of differential expression of human HL in liver, pancreas, testis, heart, skeletal muscle and brain that can help us to understand the consequences of this deficiency (Puisac et al., 2010). It is an autosomal recessive disease caused by mutations in the HMGCL gene. The study of these mutations and patients origin helps to draw a map of incidence in which three countries stand out for their high frequency: Saudi Arabia (Ozand et al., 1992), Spain and Portugal (Menao et al., 2009). At present, the functional study of missense mutations is possible thanks to the knowledge of the structure (Fu et al., 2006) and mechanism of the enzyme (Fu et al., 2010) and also by the development of a method of simple and efficient expression of the protein (Menao et al., 2009). Finally, despite the current knowledge of the disease, genotype-phenotype correlations are difficult to establish.

[8] Global and Targeted Metabolomics for Revealing Metabolomic Alteration in Niemann-Pick Disease Type C Model Cells

• Authors: Masahiro Watanabe, Masamitsu Maekawa, Keitaro Miyoshi, Toshihiro Sato, Yu Sato et al.
• Year: 2024
• Venue: Metabolites
• URL: https://www.semanticscholar.org/paper/27c7aa8f74e2997a59b92b38aec1fb9ff9cbb608
• DOI: 10.3390/metabo14100515
• PMID: 39452896
• PMCID: 11509386
• Citations: 2
• Summary: Several metabolite characteristics of Niemann-Pick disease type C that may fluctuate in a cellular model of the disease are identified using both global and targeted metabolomic analyses by liquid chromatography/tandem mass spectrometry.
• Evidence snippets:
• Snippet 1 (score: 0.444) > Background: Niemann-Pick disease type C (NPC) is an inherited disorder characterized by a functional deficiency of cholesterol transport proteins. However, the molecular mechanisms and pathophysiology of the disease remain unknown. Methods: In this study, we identified several metabolite characteristics of NPC that may fluctuate in a cellular model of the disease, using both global and targeted metabolomic analyses by liquid chromatography/tandem mass spectrometry (LC-MS/MS). Three cell lines, HepG2 cells (wild-type[WT]) and two NPC model HepG2 cell lines in which NPC1 was genetically ablated (knockout [KO]1 and KO2), were used for metabolomic analysis. Data were subjected to enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results: The enrichment analysis of global metabolomics revealed that 8 pathways in KO1 and 16 pathways in KO2 cells were notably altered. In targeted metabolomics for 15 metabolites, 4 metabolites in KO1 and 10 metabolites in KO2 exhibited statistically significant quantitative changes in KO1 or KO2 relative to WT. Most of the altered metabolites were related to creatinine synthesis and cysteine metabolism pathways. Conclusions: In the future, our objective will be to elucidate the relationship between these metabolic alterations and pathophysiology.

[9] 18O-assisted dynamic metabolomics for individualized diagnostics and treatment of human diseases

• Authors: E. Nemutlu, Song Zhang, N. Juranic, A. Terzic, S. Macura et al.
• Year: 2012
• Venue: Croatian Medical Journal
• URL: https://www.semanticscholar.org/paper/880f053c7f060db4b990e447d0a22c4b69372ddb
• DOI: 10.3325/cmj.2012.53.529
• PMID: 23275318
• PMCID: 3541579
• Citations: 28
• Summary: The potential use of dynamic phosphometabolomic platform for disease diagnostics currently under development at Mayo Clinic is described and discussed briefly.
• Evidence snippets:
• Snippet 1 (score: 0.436) > Living cells represent an integrated and interacting network of genes, transcripts, proteins, small signaling molecules, and metabolites that define cellular phenotype and function. Traditionally the focus of biomedical research was on individual genes, single protein targets, single metabolites, and metabolic or signaling pathways. This "molecular reductionist" paradigm was based on the assumption that identifying genetic variations and molecular components would lead to discovery of cures for human diseases. However, most of diseases are complex and multi-factorial and the disease phenotype is determined by the alterations of multiple genes, pathways, proteins and metabolites (at cellular, tissue, and organismal levels). Therefore, an integrated "omics" approach is more viable direction for uncovering alterations in metabolic networks, disease mechanisms, and mechanisms of drug effects. > Recent advent of large-scale metabolomics and fluxomic (metabolite dynamics and metabolic flux analysis) completed the "omics revolution" (Figure 1), where genomics, transcriptomics, proteomics, metabolomics, and fluxomics all together complement phenotype determination of living organism. Such integrated "omics" cascades provide a framework for advances in system and network biology, integrative physiology, and system medicine as well as system pharmacology and regenerative medicine. Noteworthy is the "reverse omic" approach or "metabolomicsinformed pharmacogenomics, " where discovery of specific metabolite changes have led to discovery of genetic alterations (2). Therefore, bringing new "omics" technologies to clinical practice will improve disease diagnostics and treatment by targeting drugs and procedures for each unique transcriptomic and metabolomic profiles.

[10] Short-chain enoyl-CoA hydratase deficiency causes prominent ketoacidosis with normal plasma lactate levels: A case report

• Authors: Madoka Uesugi, Jun Mori, S. Fukuhara, N. Fujii, Tadaki Omae et al.
• Year: 2020
• Venue: Molecular Genetics and Metabolism Reports
• URL: https://www.semanticscholar.org/paper/872b6da3f3bccf1ecdc81939f3fa8209fb3263c4
• DOI: 10.1016/j.ymgmr.2020.100672
• PMID: 33163364
• PMCID: 7606867
• Citations: 8
• Influential citations: 1
• Summary: A 7-month-old boy with Short-chain enoyl-CoA hydratase (ECHS1) deficiency concomitant with prominent ketoacidosis, and no elevation in plasma lactate levels is reported, expanding the understanding of the multiple symptoms of ECHS1 deficiency and emphasizing the importance of genetic testing for inborn errors of metabolism to initiate early treatment.
• Evidence snippets:
• Snippet 1 (score: 0.431) > ECHS1 is a mitochondrial enzyme that catalyzes reactions in multiple metabolic pathways, such as fatty acid ß oxidation and degradation of branched-chain amino acids (valine, leucine, and isoleucine). ECHS1 deficiency was first reported in Leigh syndrome by Peters in 2014 [1]. Since then, ~50 cases of ECHS1 deficiency have been reported. The symptoms and findings of ECHS1 deficiency vary and the frequency of ECHS1 deficiency is rare. Even patients with identical genotypes have different symptoms. The main pathophysiology of ECHS1 deficiency involves the accumulation of toxic intermediate metabolites, such as methacrylyl-CoA, in the mitochondria in the valine catabolic pathway. Patients with ECHS1 deficiency have characteristic symptoms, such as severely delayed psychomotor development, nystagmus, hyperlactatemia, and brain lesions in the basal ganglia, with metabolic acidosis and variable ketosis. This case report is based on a 7-month-old boy with ECHS1 deficiency and chief complaints of conjugate deviation and hypotonia. The findings upon examination of the patient were: 1) remarkable ketoacidosis and 2) normal levels of lactate in the blood and cerebrospinal fluid. > The 3-hydroxyisobutyryl-CoA hydrolase gene (HIBCH) is located downstream of ECHS1 in the valine metabolic pathway. Thus, patients with ECHS1 deficiency show symptoms similar to those in patients with a deficiency of HIBCH. Patients with HIBCH deficiency exhibit marked ketoacidosis during stress conditions, such as fever. This can be attributed to the enhanced supply of ATP to the brain owing to fatty acid ß oxidation. In contrast, ketoacidosis is not always observed with patients with ECHS1 deficiency. Some patients with ECHS1 deficiency have ketoacidosis [4], but some patients with ECHS1 deficiency do not present with prominent ketoacidosis due to the impairment of short-chain fatty acid β oxidation and inability to produce ketone bodies [5]. It has recently been reported that ECHS1 is less involved in isoleucine metabolism and fatty acid ß oxidation [7].

[11] Frontiers in metabolic physiology grand challenges

• Authors: J. Imig
• Year: 2022
• Venue: Frontiers in Physiology
• URL: https://www.semanticscholar.org/paper/19e2780d459288513f034516e0a7d5fa4e12298f
• DOI: 10.3389/fphys.2022.879617
• PMID: 36035475
• PMCID: 9399398
• Citations: 1
• Summary: In this chapter seven subsequent studies of the determinants of infectious disease in eight operation rooms were studied.
• Evidence snippets:
• Snippet 1 (score: 0.430) > Research in this area will identify novel therapeutic targets for diabetic complications at the levels of transcription and translation, protein expression and activity, and cell and organ levels. Major challenges in diabetes include defining molecular mechanisms and pathways implicated in insulin metabolism, evaluating transcriptomics of high glucose on different cell types, defining the contribution of the innate immune response and NLRP3 inflammasome, understanding metabolic mechanisms that drive beta cell dysfunction, and defining metabolic processes in key insulin-target tissues. > NAFLD is a rapidly growing public health concern that occurs in 25% of the world population and is driven in large part by the obesity and type 2 diabetes epidemic (Caussy et al., 2021;Targher et al., 2021). Intriguingly, NAFLD can be as high as 75% in diabetic patients (Caussy et al., 2021;Targher et al., 2021). Non-alcoholic steatosis (NASH) is a type of NAFLD that is associated with inflammation and hepatocyte lipotoxicity which leads to liver fibrosis and cancer (Caussy et al., 2021;Targher et al., 2021). NASH is expected to become the leading cause for liver transplantation in the next decade (Nephew and Serper, 2021). Mechanisms that contribute to NAFLD and progression to NASH include regulation of de novo lipogenesis by acetyl-CoA carboxylase, regulation of bile acid signaling by farnesoid X receptor (FXR), or oxidative stress induced fibrogenesis and inflammation by apoptosis signal-regulating kinase 1 (ASK1) (Attia et al., 2021;Koo and Han, 2021). Although often associated with obesity and diabetes, understanding pathophysiological mechanisms at the cellular hepatocyte and organ liver levels that result in NAFLD and progression to NASH will be key to developing therapeutics. > Major challenges to the epidemic of metabolic diseases are the focus of several publications in Frontiers in Metabolic Physiology. Studies in mice with type 2 diabetes have revealed metabolites involved in diabetic kidney disease.

[12] Cellular and molecular mechanisms of aspartoacylase and its role in Canavan disease

• Authors: Martin Grønbæk-Thygesen, R. Hartmann-Petersen
• Year: 2024
• Venue: Cell & Bioscience
• URL: https://www.semanticscholar.org/paper/d2dfbaee9666d4b1f681d466dae63d5a770fd34a
• DOI: 10.1186/s13578-024-01224-6
• PMID: 38582917
• PMCID: 10998430
• Citations: 7
• Summary: The importance of high-throughput technologies and computational prediction tools for making genotype–phenotype predictions as they await the results of ongoing trials with gene therapy for Canavan disease is highlighted.
• Evidence snippets:
• Snippet 1 (score: 0.424) > Canavan disease is an autosomal recessive and lethal neurological disorder, characterized by the spongy degeneration of the white matter in the brain. The disease is caused by a deficiency of the cytosolic aspartoacylase (ASPA) enzyme, which catalyzes the hydrolysis of N-acetyl-aspartate (NAA), an abundant brain metabolite, into aspartate and acetate. On the physiological level, the mechanism of pathogenicity remains somewhat obscure, with multiple, not mutually exclusive, suggested hypotheses. At the molecular level, recent studies have shown that most disease linked ASPA gene variants lead to a structural destabilization and subsequent proteasomal degradation of the ASPA protein variants, and accordingly Canavan disease should in general be considered a protein misfolding disorder. Here, we comprehensively summarize the molecular and cell biology of ASPA, with a particular focus on disease-linked gene variants and the pathophysiology of Canavan disease. We highlight the importance of high-throughput technologies and computational prediction tools for making genotype–phenotype predictions as we await the results of ongoing trials with gene therapy for Canavan disease.

[13] iPSC‐Derived Liver Organoids as a Tool to Study Medium Chain Acyl‐CoA Dehydrogenase Deficiency

• Authors: L. A. Kiyuna, José M. Horcas-Nieto, Christoff Odendaal, Miriam Langelaar-Makkinje, A. Gerding et al.
• Year: 2025
• Venue: Journal of Inherited Metabolic Disease
• URL: https://www.semanticscholar.org/paper/547bf305207cfd00da79f778ed7ea7eb255f1018
• DOI: 10.1002/jimd.70028
• PMID: 40199742
• PMCID: 11978564
• Citations: 2
• Summary: iPSC‐derived organoids of MCADD patients recapitulated the major biochemical phenotype of the disease, and this patient‐specific hepatic organoid system is a promising platform to study the phenotypic heterogeneity between MCADD patients.
• Evidence snippets:
• Snippet 1 (score: 0.423) > Medium chain acyl‐CoA dehydrogenase deficiency (MCADD) is an inherited metabolic disease, characterized by biallelic variants in the ACADM gene. Interestingly, even with the same genotype, patients often present with very heterogeneous symptoms, ranging from fully asymptomatic to life‐threatening hypoketotic hypoglycemia. The mechanisms underlying this heterogeneity remain unclear. Therefore, there is a need for in vitro models of MCADD that recapitulate the clinical phenotype as a tool to study the pathophysiology of the disease. Fibroblasts of control and symptomatic MCADD patients with the c.985A>G (p.K329E) were reprogrammed into induced pluripotent stem cells (iPSCs). iPSCs were then differentiated into hepatic expandable organoids (EHOs), further matured to Mat‐EHOs, and functionally characterized. EHOs and Mat‐EHOs performed typical hepatic metabolic functions, such as albumin and urea production. The organoids metabolized fatty acids, as confirmed by acyl‐carnitine profiling and high‐resolution respirometry. MCAD protein was fully ablated in MCADD organoids, in agreement with the instability of the mutated MCAD protein. MCADD organoids accumulated medium‐chain acyl‐carnitines, with a strongly elevated C8/C10 ratio, characteristic of the biochemical phenotype of the disease. Notably, C2 and C14 acyl‐carnitines were found decreased in MCADD Mat‐EHOs. Finally, MCADD organoids exhibited differential expression of genes involved in ω‐oxidation, mitochondrial β‐oxidation, TCA cycle, and peroxisomal coenzyme A metabolism, particularly upregulation of NUDT7. iPSC‐derived organoids of MCADD patients recapitulated the major biochemical phenotype of the disease. Mat‐EHOs expressed relevant pathways involved in putative compensatory mechanisms, notably CoA metabolism and the TCA cycle. The upregulation of NUDT7 expression may play a role in preventing excessive accumulation of dicarboxylic acids

[14] Investigating the Transition of Pre-Symptomatic to Symptomatic Huntington’s Disease Status Based on Omics Data

• Authors: Christiana C. Christodoulou, M. Zachariou, Marios Tomazou, E. Karatzas, C. Demetriou et al.
• Year: 2020
• Venue: International Journal of Molecular Sciences
• URL: https://www.semanticscholar.org/paper/04a48e68a0a0ad9eca22aeffdb8c22c7fb41ed86
• DOI: 10.3390/ijms21197414
• PMID: 33049985
• PMCID: 7582902
• Citations: 26
• Influential citations: 2
• Summary: The genes, pathways and metabolites identified for each HD stage can provide a better understanding of the mechanisms that become altered in each disease stage, leading to an improvement in clinical symptoms and hopefully a delay in the age of onset.
• Evidence snippets:
• Snippet 1 (score: 0.423) > HD is a monogenetic and incurable disease and at the same time its molecular manifestations remain highly complex and involve multiple cellular processes, genes, and metabolites, which needs to be investigated to understand HD pathology. Systems bioinformatics (SB) allows the integration of different biological omics data to better understand the biological pathways, mechanisms, genes and metabolites involved in HD and lead to possible therapeutic treatments and biomarker discovery. > SB is an interdisciplinary field which combines the research fields of systems biology and bioinformatics. SB allows the integration of biological data across the omics categories such a genomics, transcriptomics, proteomics, metabolomics, lipidomics, epigenomics and several types of omics data [7]. > A major approach in this direction is the generation and construction of biological networks representing each level of omics data and their integration in a layered network that permits the exchange of information between and within the layers. The goal is to reveal synergistic relationships among numerous factors rather than explore each entity individually. This data integration approach results in the construction of highly complex molecular interaction networks. The biological data, obtained through large-scale omics analysis can provide a better understanding into biological mechanisms and pathways and how a dysfunction in these mechanisms and pathways can cause the disease [7]. Furthermore, the emerging importance of biological network-based approaches, allows for potential biological and clinical applications by suggesting an intuitive and trustworthy approach to explore the biological and molecular complexity of a disease of interest [8]. > The metabolome is defined as the complete set of small chemical molecules found within a biological samples (urine, cerebrospinal fluid (CSF), serum, plasma), tissues and cells. Changes and interactions in gene and protein expression and the environment are directly revealed in the metabolome making it more chemically and physically complex than the genome, transcriptome and proteome. Metabolites are affected by the upstream influence of the genome, proteome, environmental and lifestyle factors, as well as medication and underlying diseases [9]. > Metabolomics is an omics category focused in the study of metabolites. Metabolites are defined as small biological and low molecular weight (<1500 Da) compounds, they are the end-products of metabolism [10].

[15] Exome and genome sequencing: a revolution for the discovery and diagnosis of monogenic disorders

• Authors: H. Stranneheim, A. Wedell
• Year: 2016
• Venue: Journal of Internal Medicine
• URL: https://www.semanticscholar.org/paper/112c148c6b98b6d169cd0b67d258f97d4a225a8d
• DOI: 10.1111/joim.12399
• PMID: 26250718
• Citations: 92
• Influential citations: 3
• Summary: Not only can rapid and safe diagnostics of virtually all known single‐gene defects now be established, but novel causes of disease in previously unsolved cases can also be identified, illuminating novel pathways important for normal physiology.
• Evidence snippets:
• Snippet 1 (score: 0.422) > Deficiency of 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) is a rare defect in the valine catabolic pathway associated with severe brain damage (Leigh-like disease). Mutations in the HIBCH gene were excluded in patients with a remarkably similar biochemical and clinical phenotype. Exome sequencing instead identified mutations in the ECHS1 gene-encoding short-chain enoyl-CoA hydratase. This mitochondrial enzyme is active immediately upstream of HIBCH in the valine degradation pathway [30]. > A subgroup amongst IEMs is the mitochondrial disorders, resulting from impaired oxidative phosphorylation. These disorders affect at least 1 in 5000 live births [31] and can be caused by mutations in nuclear genes or in mitochondrial DNA (mtDNA). The small, circular mtDNA molecule encodes 13 components of the respiratory chain as well as two ribosomal RNAs (rRNAs) and 22 transfer RNAs (tRNAs) required for the mitochondrial translational machinery. The majority of the subunits of the respiratory chain as well as factors required for maintenance and expression of mtDNA, including replication, transcription and translation, are encoded by nuclear DNA genes (Fig. 2). Mitochondrial function thus depends critically on the coordinated expression of genes from two genomes. Around 100 mostly recessive nuclear genes are currently known to cause mitochondrial disorders, and the number is increasing rapidly due in large part to exome sequencing. > One approach to facilitating the discovery of novel disease genes causing mitochondrial disorders relied on specifically targeting the mitochondrial genome together with all coding exons of the approximately 1000 nuclear genes known to be located inside the mitochondria in patients with biochemical evidence of impaired mitochondrial function [32]. More important, however, is the possibility of directly combining biochemical and genetic investigations. Detailed characterization of mitochondrial respiratory chain function in cells or tissue from affected patients can locate defects such as in the synthesis or assembly of specific enzyme complexes required for oxidative phosphorylation, providing a functional framework to aid the identification and validation of pathogenic variants. For example, when mutations in NDUFB3, encoding a subunit of complex I, were identified in a patient with severe, early lethal mitochondrial disease due to complex I deficiency, these could indeed be considered pathogenic even though mutations in this gene had not previously been reported

[16] New therapeutic targets in rare genetic skeletal diseases

• Authors: M. Briggs, Peter A. Bell, M. Wright, K. A. Pirog
• Year: 2015
• Venue: Expert Opinion on Orphan Drugs
• URL: https://www.semanticscholar.org/paper/1363107f71ae6d2d60abca471cddf3da5d13644b
• DOI: 10.1517/21678707.2015.1083853
• PMID: 26635999
• PMCID: 4643203
• Citations: 37
• Influential citations: 1
• Summary: An overview of disease mechanisms that are shared amongst groups of different GSDs and potential therapeutic approaches that are under investigation are described to generate critical mass for the identification and validation of novel therapeutic targets and biomarkers.
• Evidence snippets:
• Snippet 1 (score: 0.422) > proteins of the cartilage ECM such as type II collagen [50]. However, emerging knowledge suggests that the primary genetic defect may be less important than the cells' response to the expression of the mutant gene product [107]. Moreover, the largely overlooked response of a cell (i.e. chondrocyte) to the abnormal extracellular environment is also important for disease progression as illustrated by several GSDs discussed in this review. > It is important that 'omics'-based approaches and technologies are systematically applied to the study of rare GSDs so that definitive reference profiles and disease signatures are generated for each phenotype. These can then be used in a Systems Biology approach to identify both common and dissimilar pathological signatures and disease mechanisms. This approach is entirely dependent upon relevant in vitro and in vivo models (and also novel 'disease-mechanism phenocopies' [107]) for testing new diagnostic and prognostic tools and for determining the molecular mechanisms that underpin the pathophysiology so that effective therapeutic treatments can be developed and validated. This approach will eventually lead to personalized treatments and care strategies centred on shared disease mechanisms with the use of relevant biomarkers to monitor the efficacy of treatment and disease progression. > It is vital that all relevant stakeholders are involved from the outset in defining the appropriate outcomes of any potential therapeutic regime. The perceptions of a successful therapy can differ widely between the clinical academic community and the relevant patient-support groups and it is vital that there is engagement on all these issues. > In summary, the identification of causative genes and mutations for GSDs over the last 20 years, coupled with the generation and in-depth analysis of a plethora of relevant cell and mouse models, has derived new knowledge on disease mechanisms and suggested potential therapeutic targets. The fast-evolving hypothesis that clinically disparate diseases can share common disease mechanisms is a powerful concept that will generate critical mass for the identification and validation of novel therapeutic targets and biomarkers.

[17] [Retracted] Identification of HIBCH as a Fatty Acid Metabolism‐Related Biomarker in Aortic Valve Calcification Using Bioinformatics

• Authors: Jun-Yu Chen, Ya-Ru Sun, Tao Xiong, Guan-nan Wang, Qing Chang
• Year: 2022
• Venue: Oxidative Medicine and Cellular Longevity
• URL: https://www.semanticscholar.org/paper/8d3a50a11e93a0e40c20eac1e66ea3b47476e3e1
• DOI: 10.1155/2022/9558713
• Citations: 1
• Summary: 3‐hydroxyisobutyryl‐CoA hydrolase (HIBCH) was a biomarker of fatty acid metabolism‐related genes in AVC and could be applied as a diagnostic marker for AVC.
• Evidence snippets:
• Snippet 1 (score: 0.419) > The results showed a significant association between HIBCH and various immune cells. This suggests that HIBCH in AVC tissues might impact the Oxidative Medicine and Cellular Longevity progression of AVC by affecting immune cells. The GSEA results provide additional evidence that HIBCH may act through immune-related pathways. HIBCH (3-hydroxyisobutyryl-CoA hydrolase) is an enzyme that catalyzes the conversion of 3-hydroxyisobutyryl-CoA to 3-hydroxyisobutyric acid [27]. It is a key mitochondrial protein required for valine catabolism [28,29]. The metabolite 3-hydroxyisobutyric acid is transformed further to succinyl coenzyme A which is involved in the metabolism of the tricarboxylic acid (TCA) cycle. A prior research has reported the involvement of HIBCH in hepatic mitochondrial fatty acid oxidation [30]. Various studies have shown that HIBCH is related to colorectal cancer [31], ovarian cancer [32,33], prostate cancer [34], paroxysmal dyskinesia [35], and Leigh syndrome [36][37][38][39]. In addition, a study also showed the association between HIBCH with AVC [40]. > With the advancement in the understanding of AVC, various researches have demonstrated the effect of immune cells in AVC. Previous studies have shown that both antigen-presenting cells (APCs) and macrophages exist in normal and pathologic valves, but the existence of T lymphocytes is characteristic of both aging and pathologic valves [41,42]. This lymphocytic infiltration is accompanied by increased neointima formation and osteogenesis, which is the hallmark and pathological signs of AVC [43]. The abundance of B lymphocytes in the valves is related to increased disease severity. In addition, prior researches showed that the depletion of natural killer T (NKT) cells can lead to improvement or worsening of a variety of fibrotic diseases [44][45][46].

[18] Exome sequencing and metabolomic analysis of a chronic kidney disease and hearing loss patient family revealed RMND1 mutation induced sphingolipid metabolism defects

• Authors: Nagwa Gaboon, B. Banaganapalli, K. Nasser, M. Razeeth, Mosab S. Alsaedi et al.
• Year: 2019
• Venue: Saudi Journal of Biological Sciences
• URL: https://www.semanticscholar.org/paper/f1f1341fd61e31f39a5129e7c80ff67cd0b6fb0f
• DOI: 10.1016/j.sjbs.2019.10.001
• PMID: 31889854
• PMCID: 6933272
• Citations: 17
• Influential citations: 1
• Summary: Genetic defects in RMND1 gene alters the mitochondrial energy metabolism leading to the accumulation of ceramide, and subsequently promote dysregulated apoptosis and tissue necrosis in kidneys, this study suggests.
• Evidence snippets:
• Snippet 1 (score: 0.416) > One of the recently identified nuclear genes involved in mitochondrial respiratory chain deficiencies is RMND1 (Required for Meiotic Nuclear Division protein 1) (Garcia-Diaz et al., 2012;Janer et al., 2012). It has been demonstrated that various novel and common recessive mutations in RMND1 are associated with multiple phenotypes characterized by delayed maturation of vision, developmental delay, dilated cardiomyopathy, deafness and neurological defects (Gupta et al., 2016), renal tubular acidosis type 4 presented as hyponatraemia and hyperkalaemia and cystic/hypoplastic kidneys (Ng et al., 2016). Likewise, complex clinical spectrum of patients with RMND1 mutations is emerging with infantile encephalomyopathy with lactic acidosis (Garcia-Diaz et al., 2012;Casey et al., 2016) to a less severe form of developmental delay, hypotonia, renal disease and congenital sensorineural deafness (Janer et al., 2015). Therefore, molecular screening of RMND1 gene will help identify the inheritance mode of causative genetic mutations in patients with renal and or neurological defects. > MIDs have complex etiologies with underlying cross talk of inter and intra molecular signaling. Hence, metabolomic studies on these patients could provide a better understanding of the interconnectivity between genetic and molecular networks (Davies, 2018). Metabolomic profiling examines the metabolic changes in body fluids driven from cellular processes to understand the onset and pathogenesis of disease phenotype (Abbiss et al., 2019). Metabolomics analyzes metabolites by either targeted or untargeted approaches. The untargeted approach involves hypothesis free surveying of hundreds of thousands of small molecule metabolites for discovering novel mechanisms or pathways, whereas the targeted one refers to measuring predefined sets of metabolites, typically focusing on a few pathways of interest (Kalim and Rhee, 2017). The specific relationship between inherited mutations in mitochondrial proteins and their functional impacts in terms of metabolic defects in chronic kidney disease (CKD) is not yet well characterized.

[19] Diet and Nutrients in Rare Neurological Disorders: Biological, Biochemical, and Pathophysiological Evidence

• Authors: Marilena Briglia, Fabio Allia, R. Avola, C. Signorini, V. Cardile et al.
• Year: 2024
• Venue: Nutrients
• URL: https://www.semanticscholar.org/paper/7308e8ab8a771741ed66510938d52004f1d64f92
• DOI: 10.3390/nu16183114
• PMID: 39339713
• PMCID: 11435074
• Citations: 5
• Influential citations: 1
• Summary: This work aims to collect the in vitro, in vivo, and clinical evidence on the effects of diet and of nutrient intake on some rare neurological disorders, including some genetic diseases, and rare brain tumors.
• Evidence snippets:
• Snippet 1 (score: 0.414) > The relationship between nutritional intervention and rare neurological diseases is an emerging area of research that may help to manage symptoms, slow disease progression, or even impact the underlying mechanisms of certain rare neurological disorders. The complex interactions between diet and neurological health require closer interdisciplinary collaboration between neurologists, dietitians, and researchers. It is crucial to conduct clinical trials to establish evidence-based dietary guidelines tailored to these unique conditions. > The major limits of this study reside in (i) the limited availability of data as accessed by the reduced number of published papers and completed clinical trials; (ii) the wide variety of the disease's pathophysiology; and (iii) the underestimated influence of individual variability due to genetic differences. Specifically, as reported above, the number of published articles about the role of diet/nutrients on rare neurological disorders is very small. Clinical data often derive from case reports or small cohort studies, and large-scale clinical trials are scarce due to the rarity of these disorders and the huge heterogeneity of clinical manifestations. Moreover, even if preclinical studies give us more detailed information about the impact of a specific nutrient on cell function or on disease progression in animal models, they could not reflect the patients' individual genetic differences and their global health status (e.g., integrity of anatomic structure and physiological functions as blood circulation, pressure, constipation, and deglutition). These latter aspects may influence how patients assume, metabolize, and distribute nutrients as has been discussed above in the case of cholesterol supplementation for Pelizaeus-Merzbacher disease. > Moreover, when searching for dietary recommendations for a specific rare disease, a great limit is the heterogeneity of occurring genetic mutations that turn into different phenotypes and clinical manifestations. Briefly, what could work for one patient may not be effective for another one, as enlightened here by reviewing the activity of trihydroxyisoflavone as a GALC-addressed chaperone. > However, despite these limitations, our analysis has, as a strength, the identification of a specific dietary intervention for the management of some symptoms. It is the case of ketogenic diet efficacy in different rare neurological diseases that share epilepsy in clinical manifestation.

[20] HMG-CoA Lyase Deficiency: A Retrospective Study of 62 Saudi Patients

• Authors: M. Alfadhel, Basma Abadel, Hind Almaghthawi, Muhammad Umair, Z. Rahbeeni et al.
• Year: 2022
• Venue: Frontiers in Genetics
• URL: https://www.semanticscholar.org/paper/080356c04b117156bf7674f3ee445870810ce7d0
• DOI: 10.3389/fgene.2022.880464
• PMID: 35646072
• PMCID: 9136170
• Citations: 17
• Influential citations: 1
• Summary: This is the largest cohort of HMGCLD patients reported from Saudi Arabia, signifying this disorder as a likely life-threatening disease, with a high prevalence in the region, and suggest that diagnosis at an early stage with careful dietary management may avoid metabolic crises.
• Evidence snippets:
• Snippet 1 (score: 0.409) > Therefore, the first step in the diagnosis is the MS/MS-based acylcarnitine profiling in dried blood spots/ plasma and the GC/MS-based organic acid analysis in urine. Then, molecular diagnosis using next-generation sequencing should be followed to pinpoint precisely the genetic cause of the disease (Ismail et al., 2019;Alfadhel et al., 2021). Similarly, targeted newborn screening should also be initiated to eradicate this severe disorder (Alfadhel et al., 2019). Similarly, Reimão et al. described late-onset diseases where a 29-year-old man with no prior medical history presented sudden coma, profound hypoglycemia, hyperammonemia, and metabolic acidosis without ketosis (Reimão et al., 2009). A 36-year-old woman with a late onset of an acute episode of hyperproteinorachia (0.73 g/L), hypoglycemia, and generalized seizures has also been reported (Bischof et al., 2004). Patients showing different levels of severity in the phenotypic presentation highlight clinical heterogeneity of the disorder. Still, it is difficult to establish a proper genotype-phenotype correlation in HMGCLD (Grunert et al., 2017;Alfadhel et al., 2017). > The mechanism of neurological pathophysiology in HGMCLD is still poorly understood; however, possible mechanisms include secondary carnitine deficiency, hypoketotic hypoglycemia, and intracellular toxic organic acid accumulation (Puisac et al., 2010). Furthermore, MRI shows a unique combination of T2-weighted diffuse mild signal changes with multiple foci of a more severe signal abnormality (Legault et al., 2015). Studies using rats revealed that the accumulation of the metabolite in HMGCLD results in oxidative stress in the developing rat's striatum that disrupts bioenergetics dynamics, signaling pathways, and ER-mitochondria communication, which might explain the HMGCLD disease pathogenesis (da Rosa et al., 2015).

Notes

• This provider combines search_papers_by_relevance with snippet_search.
• No synthesis or second-stage model call is performed.

Question: You are an expert researcher providing comprehensive, well-cited information.

Provide detailed information focusing on: 1. Key concepts and definitions with current understanding 2. Recent developments and latest research (prioritize 2023-2024 sources) 3. Current applications and real-world implementations 4. Expert opinions and analysis from authoritative sources 5. Relevant statistics and data from recent studies

Format as a comprehensive research report with proper citations. Include URLs and publication dates where available. Always prioritize recent, authoritative sources and provide specific citations for all major claims.

Disease Characteristics Research Template

Target Disease

• Disease Name: 3-hydroxyisobutyryl-CoA hydrolase deficiency
• MONDO ID: (if available)
• Category: Mendelian

Research Objectives

Please provide a comprehensive research report on 3-hydroxyisobutyryl-CoA hydrolase deficiency covering all of the disease characteristics listed below. This report will be used to populate a disease knowledge base entry. Be thorough and cite primary literature (PMID preferred) for all claims.

For each section, suggested databases/resources are listed. These are the first places you should search for information on each topic.

1. Disease Information

Search first: OMIM, Orphanet, ICD-10/ICD-11, MeSH, PubMed

• What is the disease? Provide a concise overview.
• What are the key identifiers? (OMIM, Orphanet, ICD-10/ICD-11, MeSH, Mondo)
• What are the common synonyms and alternative names?
• Is the information derived from individual patients (e.g., EHR) or aggregated disease-level resources?

2. Etiology

• Disease Causal Factors: What are the primary causes? (genetic, environmental, infectious, mechanistic)
• Risk Factors:

  Search first: PubMed, Cochrane Library, UpToDate, clinical guidelines, ClinVar, ClinGen, GWAS Catalog, PheGenI, CTD, CDC, WHO, epidemiological databases

• Genetic risk factors (causal variants, susceptibility loci, modifier genes)
• Environmental risk factors (toxins, lifestyle, occupational exposures, age, sex, family history)
• Protective Factors:

  Search first: PubMed, Cochrane Library, clinical trial databases, GWAS Catalog, gnomAD, WHO, CDC, nutrition databases

• Genetic protective factors (protective variants, modifier alleles)
• Environmental protective factors (diet, lifestyle, exposures that reduce risk)
• Gene-Environment Interactions: How do genetic and environmental factors interact to influence disease?

  Search first: CTD, PubMed, PheGenI, GxE databases

3. Phenotypes

Search first: HPO (Human Phenotype Ontology), OMIM, Orphanet, PubMed, clinicaltrials.gov, MedDRA, SNOMED CT, DECIPHER, LOINC

For each phenotype, provide: - Phenotype type: symptoms, clinical signs, physical manifestations, behavioral changes, or laboratory abnormalities

For symptoms/signs: HPO, OMIM, Orphanet, PubMed For behavioral changes: HPO, DSM, RDoC (Research Domain Criteria), PubMed For laboratory abnormalities: LOINC, SNOMED CT, LabTests Online, PubMed - Phenotype characteristics: Search first: OMIM, Orphanet, HPO, PubMed - Age of symptom onset (neonatal, childhood, adult-onset, late-onset) - Symptom severity (mild, moderate, severe, variable) - Symptom progression (stable, progressive, episodic, fluctuating) - Frequency among affected individuals (percentage or qualitative) - Quality of life impact: Effects on daily functioning and well-being (per-phenotype when possible) Search first: EQ-5D database, SF-36, WHO QOL databases, PubMed - Suggest HPO (Human Phenotype Ontology) terms for each phenotype

4. Genetic/Molecular Information

• Causal Genes: Gene mutations or chromosomal abnormalities responsible for disease (gene symbols, OMIM IDs)

  Search first: OMIM, ClinVar, HGMD, Ensembl, NCBI Gene

• Pathogenic Variants:
• Affected genes (gene symbols, HGNC IDs) > Search first: OMIM, NCBI Gene, Ensembl, HGNC, UniProt, GeneCards
• Variant classification (pathogenic, likely pathogenic, VUS per ACMG/AMP guidelines) > Search first: ClinVar, ClinGen, ACMG/AMP guidelines, VarSome
• Variant type/class (missense, frameshift, nonsense, splice-site, structural)
• Allele frequency in population databases > Search first: gnomAD, 1000 Genomes, ExAC, TOPMed, dbSNP
• Somatic vs germline origin > Search first: COSMIC (somatic), ClinVar, ICGC, TCGA
• Functional consequences (loss of function, gain of function, dominant negative)
• Modifier Genes: Genes that modify disease severity or expression
• Epigenetic Information: DNA methylation, histone modifications, chromatin changes affecting disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Chromosomal Abnormalities: Large-scale genetic changes (aneuploidy, translocations, inversions)

  Search first: DECIPHER, ClinVar, ECARUCA, UCSC Genome Browser

5. Environmental Information

• Environmental Factors: Non-genetic contributing factors (toxins, radiation, pollution, occupational exposure)

  Search first: CTD (Comparative Toxicogenomics Database), TOXNET, PubMed, EPA databases

• Lifestyle Factors: Behavioral factors (smoking, diet, exercise, alcohol consumption)

  Search first: CDC databases, WHO, PubMed, NHANES

• Infectious Agents: If applicable, pathogens causing or triggering disease (bacteria, viruses, fungi, parasites)

  Search first: NCBI Taxonomy, ViPR, BV-BRC, MicrobeDB, GIDEON

6. Mechanism / Pathophysiology

• Molecular Pathways: Specific signaling cascades or biochemical pathways involved (Wnt, MAPK, mTOR, PI3K-AKT, etc.)

  Search first: KEGG, Reactome, WikiPathways, PathBank, BioCyc

• Cellular Processes: Cell-level mechanisms (apoptosis, autophagy, cell cycle dysregulation, inflammation, etc.)

  Search first: Gene Ontology (GO), Reactome, KEGG, PubMed

• Protein Dysfunction: How protein structure or function is altered (misfolding, aggregation, loss of function, gain of function)

  Search first: UniProt, PDB (Protein Data Bank), InterPro, Pfam, AlphaFold

• Metabolic Changes: Alterations in metabolic processes (energy metabolism, lipid metabolism, amino acid metabolism)

  Search first: KEGG, BioCyc, HMDB (Human Metabolome Database), BRENDA

• Immune System Involvement: Role of immune response (autoimmunity, immunodeficiency, chronic inflammation)

  Search first: ImmPort, Immunome Database, IEDB, Gene Ontology

• Tissue Damage Mechanisms: How tissues/ are injured (oxidative stress, ischemia, fibrosis, necrosis)

  Search first: PubMed, Gene Ontology, Reactome

• Biochemical Abnormalities: Specific molecular defects (enzyme deficiencies, receptor dysfunction, ion channel defects)

  Search first: BRENDA, UniProt, KEGG, OMIM, PubMed

• Epigenetic Changes: DNA methylation, histone modifications affecting gene expression in disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Molecular Profiling (if available):
• Transcriptomics/gene expression changes > Search first: GEO (Gene Expression Omnibus), ArrayExpress, GTEx, Human Cell Atlas, SRA
• Proteomics findings > Search first: PRIDE, ProteomeXchange, Human Protein Atlas, STRING, BioGRID
• Metabolomics signatures > Search first: MetaboLights, Metabolomics Workbench, HMDB, METLIN
• Lipidomics alterations > Search first: LIPID MAPS, SwissLipids, LipidHome, Metabolomics Workbench
• Genomic structural features > Search first: UCSC Genome Browser, Ensembl, NCBI, dbVar, DGV
• Advanced Technologies (if applicable):
• Single-cell analysis findings (cell-type specific mechanisms, cellular heterogeneity) > Search first: Human Cell Atlas, Single Cell Portal, GEO, CELLxGENE
• Spatial transcriptomics findings > Search first: GEO, Spatial Research, Vizgen, 10x Genomics data
• Multi-omics integration results > Search first: TCGA, ICGC, cBioPortal, LinkedOmics, PubMed
• Functional genomics screens (CRISPR, RNAi) > Search first: DepMap, GenomeRNAi, PubMed, BioGRID ORCS

For each mechanism, describe: - The causal chain from initial trigger to clinical manifestation - Which mechanisms are upstream vs downstream - What cell types and biological processes are involved - Suggest GO terms for biological processes and CL terms for cell types

7. Anatomical Structures Affected

• Organ Level:
• Primary organs directly affected
• Secondary organ involvement (complications, secondary effects)
• Body systems involved (cardiovascular, nervous, digestive, respiratory, endocrine, etc.)

  Search first: Uberon, FMA (Foundational Model of Anatomy), OMIM, HPO, ICD-11, MeSH, SNOMED CT

• Tissue and Cell Level:
• Specific tissue types affected (epithelial, connective, muscle, nervous)
• Specific cell populations targeted (with Cell Ontology terms)

  Search first: Uberon, Human Protein Atlas, Cell Ontology, Human Cell Atlas, CellMarker, PanglaoDB

• Subcellular Level:
• Cellular compartments involved (mitochondria, nucleus, ER, lysosomes) (with GO Cellular Component terms)

  Search first: Gene Ontology (Cellular Component), UniProt, Human Protein Atlas

• Localization:
• Specific anatomical sites (with UBERON terms) > Search first: FMA, Uberon, NeuroNames (for brain), SNOMED CT
• Lateralization (unilateral, bilateral, asymmetric) > Search first: HPO, clinical literature, imaging databases

8. Temporal Development

• Onset:
• Typical age of onset (congenital, pediatric, adult, geriatric)
• Onset pattern (acute, subacute, chronic, insidious)

  Search first: OMIM, Orphanet, HPO, PubMed

• Progression:
• Disease stages (early, intermediate, advanced, end-stage) > Search first: Cancer Staging Manual (AJCC), WHO classifications, PubMed
• Progression rate (rapid, slow, variable)
• Disease course pattern (episodic, relapsing-remitting, progressive, stable)
• Disease duration (self-limited, chronic lifelong)

  Search first: Disease registries, longitudinal cohort databases, natural history studies, PubMed, Orphanet, OMIM

• Patterns:
• Remission patterns (spontaneous, treatment-induced) > Search first: Clinical trial databases, disease registries, PubMed
• Critical periods (time windows of vulnerability or opportunity for intervention) > Search first: PubMed, developmental biology databases, clinical guidelines

9. Inheritance and Population

• Epidemiology:
• Prevalence (cases per 100,000 at given time)
• Incidence (new cases per 100,000 per year)

  Search first: Orphanet, CDC, WHO, GBD (Global Burden of Disease), national registries, SEER, disease registries

• For Genetic Etiology:
• Inheritance pattern (AD, AR, X-linked, mitochondrial, multifactorial, polygenic) > Search first: OMIM, Orphanet, ClinVar, GTR (Genetic Testing Registry)
• Penetrance (complete, incomplete, age-dependent) > Search first: ClinVar, OMIM, PubMed, ClinGen
• Expressivity (variable, consistent) > Search first: OMIM, ClinVar, PubMed
• Genetic anticipation (increasing severity in successive generations) > Search first: OMIM, PubMed (especially for repeat expansion disorders)
• Germline mosaicism > Search first: ClinVar, OMIM, genetic counseling literature, PubMed
• Founder effects (population-specific mutations) > Search first: gnomAD, population genetics databases, PubMed
• Consanguinity role > Search first: OMIM, population studies, genetic counseling resources
• Carrier frequency > Search first: gnomAD, carrier screening databases, GeneReviews, GTR
• Population Demographics:
• Affected populations (ethnic or demographic groups with higher prevalence) > Search first: gnomAD, 1000 Genomes, PAGE Study, PubMed, population registries
• Geographic distribution (endemic areas, regional variation) > Search first: WHO, CDC, GBD, Orphanet, geographic epidemiology databases
• Geographic distribution of specific variants
• Sex ratio (male:female) > Search first: Disease registries, OMIM, PubMed, epidemiological databases
• Age distribution of affected individuals > Search first: CDC, disease registries, SEER, Orphanet

10. Diagnostics

• Clinical Tests:
• Laboratory tests (blood, urine, tissue chemistry, specific enzyme assays) > Search first: LOINC, LabTests Online, PubMed
• Biomarkers (proteins, metabolites, genetic markers, circulating biomarkers) > Search first: FDA Biomarker List, BEST (Biomarkers, EndpointS, and other Tools), PubMed
• Imaging studies (X-ray, CT, MRI, PET, ultrasound) > Search first: RadLex, DICOM, Radiopaedia, imaging databases
• Functional tests (pulmonary function, cardiac stress tests) > Search first: LOINC, clinical guidelines, PubMed
• Electrophysiology (EEG, EMG, ECG, nerve conduction studies) > Search first: LOINC, clinical neurophysiology databases, PubMed
• Biopsy findings (histopathology, immunohistochemistry) > Search first: SNOMED CT, College of American Pathologists resources, PubMed
• Pathology findings (microscopic examination) > Search first: SNOMED CT, Digital Pathology databases, PubMed
• Genetic Testing:

  Search first: GTR (Genetic Testing Registry), GeneReviews, ClinGen

• Overview of recommended genetic testing approach
• Whole genome sequencing (WGS) utility > Search first: GTR, ClinVar, GEL (Genomics England), gnomAD
• Whole exome sequencing (WES) utility > Search first: GTR, ClinVar, OMIM, GeneMatcher
• Gene panels (which panels, which genes) > Search first: GTR, ClinVar, laboratory-specific databases
• Single gene testing > Search first: GTR, ClinVar, OMIM, GeneReviews
• Chromosomal microarray (CMA) > Search first: DECIPHER, ClinVar, dbVar, ECARUCA
• Karyotyping > Search first: Chromosome Abnormality Database, ClinVar, cytogenetics resources
• FISH > Search first: ClinVar, cytogenetics databases, PubMed
• Mitochondrial DNA testing > Search first: MITOMAP, MSeqDR, ClinVar, GTR
• Repeat expansion testing > Search first: GTR, ClinVar, repeat expansion databases, PubMed
• Omics-Based Diagnostics (if applicable):
• RNA sequencing / transcriptomics > Search first: GEO, ArrayExpress, GTEx, RNA-seq databases
• Proteomics > Search first: PRIDE, ProteomeXchange, FDA Biomarker database
• Metabolomics > Search first: MetaboLights, Metabolomics Workbench, HMDB
• Epigenomics > Search first: GEO, ENCODE, Roadmap Epigenomics, MethBase
• Liquid biopsy > Search first: COSMIC, ClinVar, liquid biopsy databases, PubMed
• Clinical Criteria:
• Standardized diagnostic criteria (DSM, ICD, society guidelines) > Search first: DSM-5, ICD-11, clinical society guidelines, UpToDate
• Differential diagnosis (other conditions to rule out, with distinguishing features) > Search first: DynaMed, UpToDate, clinical decision support systems
• Screening:
• Screening methods for asymptomatic individuals (newborn screening, carrier screening, cascade screening) > Search first: ACMG recommendations, CDC newborn screening, GTR

11. Outcome/Prognosis

• Survival and Mortality:
• Survival rate (5-year, 10-year, overall) > Search first: SEER, cancer registries, disease-specific registries, PubMed
• Life expectancy (with and without treatment if applicable) > Search first: Orphanet, disease registries, actuarial databases, PubMed
• Mortality rate > Search first: CDC, WHO, GBD, national mortality databases
• Disease-specific mortality (deaths directly attributable to disease) > Search first: Disease registries, CDC Wonder, GBD, PubMed
• Morbidity and Function:
• Morbidity (disease-related disability and health impacts) > Search first: GBD, WHO, disability databases, PubMed
• Disability outcomes (long-term functional impairments) > Search first: ICF (International Classification of Functioning), disability registries
• Quality of life measures (EQ-5D, SF-36, PROMIS, disease-specific tools) > Search first: EQ-5D database, SF-36, PROMIS, PubMed
• Disease Course:
• Complications (secondary problems: infections, organ failure, etc.) > Search first: ICD codes, disease registries, clinical databases, PubMed
• Recovery potential (likelihood and extent of recovery, with vs without treatment) > Search first: Natural history studies, rehabilitation databases, PubMed
• Prediction:
• Prognostic factors (age, disease severity, biomarkers, treatment response) > Search first: Prognostic models databases, clinical calculators, PubMed
• Prognostic biomarkers (molecular markers predicting disease course) > Search first: FDA Biomarker database, PubMed, cancer prognostic databases

12. Treatment

• Pharmacotherapy:
• Pharmacological treatments (drug names, drug classes, mechanisms of action) > Search first: DrugBank, RxNorm, ATC classification, DailyMed, FDA databases
• Pharmacogenomics (how genetic variants affect drug metabolism, efficacy, toxicity) > Search first: PharmGKB, CPIC (Clinical Pharmacogenetics), FDA Table of PGx Biomarkers
• Advanced Therapeutics:
• Gene therapy (viral vectors, CRISPR, gene replacement, gene editing) > Search first: ClinicalTrials.gov, FDA gene therapy database, ASGCT resources
• Cell therapy (stem cell transplant, CAR-T, cellular therapeutics) > Search first: ClinicalTrials.gov, FDA cell therapy database, FACT standards
• RNA-based therapies (ASOs, siRNA, mRNA therapies) > Search first: ClinicalTrials.gov, FDA approvals, PubMed
• Targeted therapies (treatments directed at specific molecular targets) > Search first: My Cancer Genome, OncoKB, ClinicalTrials.gov, FDA approvals
• Immunotherapies (checkpoint inhibitors, monoclonal antibodies) > Search first: Cancer Immunotherapy Database, FDA approvals, ClinicalTrials.gov
• Surgical and Interventional:
• Surgical interventions (types of surgery, timing, outcomes) > Search first: CPT codes, surgical registries, clinical guidelines, PubMed
• Supportive and Rehabilitative:
• Supportive care (symptom management, pain control, nutrition) > Search first: Clinical guidelines, Cochrane Library, PubMed
• Rehabilitation (physical therapy, occupational therapy, speech therapy) > Search first: Rehabilitation medicine databases, clinical guidelines, PubMed
• Experimental:
• Experimental treatments in clinical trials (with NCT identifiers if available) > Search first: ClinicalTrials.gov, EU Clinical Trials Register, WHO ICTRP
• Treatment Outcomes:
• Treatment response rates > Search first: Clinical trial databases, FDA reviews, systematic reviews, PubMed
• Side effects and adverse events > Search first: FDA Adverse Event Reporting System (FAERS), MedWatch, PubMed
• Treatment Strategy:
• Treatment algorithms (clinical pathways, decision trees) > Search first: Clinical practice guidelines, NCCN Guidelines, UpToDate
• Combination therapies > Search first: ClinicalTrials.gov, treatment guidelines, PubMed
• Personalized medicine approaches (genotype-guided treatment) > Search first: My Cancer Genome, CIViC, PharmGKB, precision medicine databases

For each treatment, suggest MAXO (Medical Action Ontology) terms where applicable.

13. Prevention

• Prevention Levels:
• Primary prevention (preventing disease occurrence: vaccination, risk factor modification) > Search first: CDC, WHO, USPSTF recommendations, Cochrane Library
• Secondary prevention (early detection and treatment: screening programs, early intervention) > Search first: USPSTF, CDC screening guidelines, WHO
• Tertiary prevention (preventing complications in those with disease) > Search first: Clinical guidelines, disease management protocols, PubMed
• Immunization: Vaccine strategies (if applicable)

  Search first: CDC vaccine schedules, WHO immunization, FDA vaccine database

• Screening and Early Detection:
• Screening programs (population-based: newborn screening, cancer screening) > Search first: CDC screening programs, USPSTF, cancer screening databases
• Genetic screening (carrier screening, preimplantation genetic diagnosis, prenatal testing) > Search first: ACMG recommendations, ACOG guidelines, GTR
• Risk stratification (identifying high-risk individuals for targeted prevention) > Search first: Risk prediction models, clinical calculators, PubMed
• Behavioral Interventions: Lifestyle modifications to reduce risk

  Search first: CDC, WHO, behavioral intervention databases, Cochrane Library

• Counseling: Genetic counseling (risk assessment, family planning guidance)

  Search first: NSGC resources, ACMG guidelines, GeneReviews

• Public Health:
• Public health interventions (sanitation, vector control, health education) > Search first: CDC, WHO, public health databases, PubMed
• Environmental interventions (reducing environmental risk factors) > Search first: EPA databases, WHO environmental health, PubMed
• Prophylaxis: Preventive medications or procedures

  Search first: Clinical guidelines, FDA approvals, PubMed

14. Other Species / Natural Disease

• Taxonomy: Species affected (with NCBI Taxon identifiers)

  Search first: NCBI Taxonomy

• Breed: Specific breeds affected (with VBO identifiers if applicable)

  Search first: VBO (Vertebrate Breed Ontology)

• Gene: Orthologous genes in other species (with NCBI Gene IDs)

  Search first: NCBI Gene

• Natural Disease:
• Naturally occurring disease in other species (companion animals, wildlife) > Search first: OMIA (Online Mendelian Inheritance in Animals), VetCompass, PubMed
• Veterinary relevance and importance in animal health > Search first: OMIA, veterinary databases, PubMed
• Comparative Biology:
• Comparative pathology (similarities and differences across species) > Search first: OMIA, comparative pathology databases, PubMed
• Evolutionary conservation of disease mechanisms > Search first: HomoloGene, OrthoMCL, Alliance of Genome Resources
• Transmission (if applicable):
• Zoonotic potential > Search first: CDC zoonotic diseases, WHO zoonoses, GIDEON
• Cross-species susceptibility > Search first: NCBI Taxonomy, veterinary databases, PubMed

15. Model Organisms

• Model Types:
• Model organism type (mammalian, invertebrate, cellular, in vitro) > Search first: Alliance of Genome Resources, model organism databases
• Specific model systems (mouse, rat, zebrafish, Drosophila, C. elegans, yeast, cell lines, organoids, iPSCs) > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, SGD, ATCC, Cellosaurus
• Induced models (drug treatment, surgical intervention, environmental manipulation) > Search first: MGI, model organism databases, PubMed
• Genetic Models:
• Types available (knockout, knock-in, transgenic, conditional, humanized) > Search first: MGI, IMPC, KOMP, EuMMCR, IMSR
• Model Characteristics:
• Phenotype recapitulation (how well model reproduces human disease features) > Search first: Model organism databases, comparative studies, PubMed
• Model limitations (aspects of human disease not captured) > Search first: Model organism databases, PubMed, review articles
• Applications:
• Research applications (what aspects of disease can be studied) > Search first: Model organism databases, PubMed
• Resources:
• Model databases > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, IMSR, EMMA, MMRRC

Citation Requirements

• Cite primary literature (PMID preferred) for all mechanistic and clinical claims
• Prioritize recent reviews and landmark papers
• Include direct quotes from abstracts where possible to support key statements
• Distinguish evidence source types: human clinical, model organism, in vitro, computational

Output Format

Structure your response as a comprehensive narrative organized by the sections above. For each section, provide: - Factual content with specific details (numbers, percentages, gene names, variant nomenclature) - Ontology term suggestions (HPO, GO, CL, UBERON, CHEBI, MAXO, MONDO) where applicable - Evidence citations with PMIDs - Direct quotes from abstracts to support key claims - Clear indication when information is not available or not applicable for this disease

This report will be used to populate a disease knowledge base entry with: - Pathophysiology descriptions with causal chains - Gene/protein annotations (HGNC, GO terms) - Phenotype associations (HP terms) with frequencies - Cell type involvement (CL terms) - Anatomical locations (UBERON terms) - Chemical entities (CHEBI terms) - Treatment annotations (MAXO terms) - Evidence items with PMIDs and exact abstract quotes - Epidemiology, prognosis, diagnostic, and prevention information - Animal model descriptions with phenotype recapitulation details

Comprehensive Research Report: 3-hydroxyisobutyryl‑CoA hydrolase deficiency (HIBCH deficiency)

Executive summary

3-hydroxyisobutyryl‑CoA hydrolase deficiency (HIBCH deficiency; HIBCHD) is an ultra-rare autosomal recessive inborn error of mitochondrial valine catabolism caused by biallelic pathogenic variants in HIBCH, typically presenting in infancy with developmental delay/regression and Leigh/Leigh-like neuroimaging (basal ganglia lesions, often globus pallidus) and characteristic metabolite abnormalities such as C4‑OH (hydroxy‑C4) acylcarnitine and valine-pathway urinary metabolites. It is frequently evaluated under the Leigh syndrome spectrum diagnostic framework, where parallel biochemical testing (acylcarnitines + urinary organic acids) alongside NGS/WES is emphasized to accelerate identification of potentially treatable causes. (stiles2015successfuldiagnosisof pages 1-3, wang2021cinicalmetabolicand pages 1-2, marti‐sanchez2021delineatingtheneurological pages 7-8, baldo2024acomprehensiveapproach pages 2-4)

1. Disease information

1.1 What is the disease?

HIBCH deficiency is an inborn error of metabolism due to impaired function of 3-hydroxyisobutyryl‑CoA hydrolase, a mitochondrial enzyme in the valine degradation pathway, leading to a Leigh/Leigh-like neurodegenerative phenotype with episodic metabolic decompensation in many patients. (stiles2015successfuldiagnosisof pages 1-3, jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, wang2021cinicalmetabolicand pages 1-2)

1.2 Key identifiers and database mappings

• OMIM (disease): 250620 (stiles2015successfuldiagnosisof pages 1-3, jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 1-2, alayed2020metabolicacidosisand pages 2-3)
• OMIM (gene): HIBCH (gene OMIM 610690) as reported in a 2024 Bahrain cohort paper (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3)
• MONDO / Orphanet / MeSH / ICD-10/ICD-11: Not identified in the retrieved full-text evidence for this run; these should be added by querying MONDO/Orphanet/MeSH/ICD resources directly in a subsequent curation step. (evidence gap)

1.3 Synonyms / alternative names

• “HIBCH deficiency”, “HIBCHD”, “3‑hydroxyisobutyryl‑CoA hydrolase deficiency”, and “Leigh/Leigh-like syndrome due to HIBCH variants” are used across the clinical genetics literature. (stiles2015successfuldiagnosisof pages 1-3, wang2021cinicalmetabolicand pages 1-2, taura2023leighlikesyndromewith pages 1-2)

1.4 Evidence sources: individual vs aggregated

The current evidence base is largely derived from case reports and small cohorts, including retrospective clinic cohorts and multi-center natural history-style aggregations. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, wang2021cinicalmetabolicand pages 1-2, marti‐sanchez2021delineatingtheneurological pages 3-5)

2. Etiology

2.1 Disease causal factors

• Primary cause: biallelic (autosomal recessive) pathogenic variants in HIBCH, disrupting mitochondrial valine catabolism. (stiles2015successfuldiagnosisof pages 1-3, alayed2020metabolicacidosisand pages 2-3)
• Mechanistic causal link: disruption of the valine pathway leads to accumulation of valine-derived intermediates (including reactive species discussed in the literature), contributing to secondary mitochondrial dysfunction/Leigh-like presentations. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, stiles2015successfuldiagnosisof pages 6-8)

2.2 Risk factors

• Genetic risk factor: being homozygous/compound heterozygous for pathogenic HIBCH variants; consanguinity is present in some pedigrees and supports autosomal recessive inheritance in affected families. (stiles2015successfuldiagnosisof pages 3-5, kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3)
• Environmental/triggering factors (precipitants): intercurrent infection/illness and metabolic stress can precipitate encephalopathy/regression in many patients with valine-pathway defects, including HIBCH deficiency. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6, taura2023leighlikesyndromewith pages 1-2)

2.3 Protective factors

No validated genetic or environmental protective factors were identified in the retrieved evidence for HIBCH deficiency. (evidence gap)

2.4 Gene–environment interactions

Evidence is largely descriptive: infections and increased metabolic demands appear to trigger decompensation, but formal gene–environment interaction studies were not identified. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6, taura2023leighlikesyndromewith pages 1-2)

3. Phenotypes (with HPO suggestions)

3.1 Core neurologic phenotype

Commonly reported manifestations include developmental delay/regression, hypotonia, encephalopathy, feeding difficulties, and movement disorders (dystonia/spasticity/ataxia), with seizures and ocular abnormalities in some patients. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, wang2021cinicalmetabolicand pages 1-2, marti‐sanchez2021delineatingtheneurological pages 7-8)

HPO term suggestions (non-exhaustive): - Developmental delay HP:0001263 - Developmental regression HP:0002376 - Hypotonia HP:0001252 - Encephalopathy HP:0001298 - Dystonia HP:0001332 - Spasticity HP:0001257 - Ataxia HP:0001251 - Seizure HP:0001250 - Feeding difficulties HP:0011968 - Optic atrophy HP:0000648 - Nystagmus HP:0000639

3.2 Age of onset and course

• Onset is typically in infancy/early childhood; one cohort reports onset “from as early as 6 weeks to 6 months” (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6), while another case-series reported median onset 13 months (8–18 months) (wang2021cinicalmetabolicand pages 1-2).
• Course is often progressive and can include episodic worsening with acute encephalopathy. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6, marti‐sanchez2021delineatingtheneurological pages 7-8)

3.3 Neuroimaging phenotype

• Leigh/Leigh-like basal ganglia involvement is typical; HIBCH deficiency can show “Bilateral globus pallidus T2-WI hyperintensities” as a distinguishing neuroradiologic pattern in comparative series. (marti‐sanchez2021delineatingtheneurological pages 7-8)
• Longitudinal imaging can show additional features, including progressive cerebellar atrophy in some cases, expanding the known phenotype. (taura2023leighlikesyndromewith pages 1-2)

HPO suggestions: - Abnormality of the basal ganglia HP:0002134 - Abnormal brain MRI signal HP:0012448 - Cerebellar atrophy HP:0001272

3.4 Laboratory abnormalities

Key biochemical findings used clinically include elevated C4‑OH acylcarnitine and urine valine-pathway metabolites (see Diagnostics). (wang2021cinicalmetabolicand pages 1-2, kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3)

3.5 Quality of life

Formal QoL instruments were not identified in the retrieved evidence, but functional outcomes can be severe (persistent developmental delay; loss of ambulation in severe cases), implying substantial QoL impact. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6, taura2023leighlikesyndromewith pages 2-3)

4. Genetic / molecular information

4.1 Causal gene and function

HIBCH catalyzes a step in valine catabolism; in a comparative natural history study, the authors state: “HIBCH catalyses the fifth step of valine catabolism” and note biochemical accumulation of 3-hydroxyisobutyrylcarnitine in HIBCH deficiency. (marti‐sanchez2021delineatingtheneurological pages 3-5, marti‐sanchez2021delineatingtheneurological pages 7-8)

4.2 Pathogenic variant spectrum (examples from cohorts)

• A 2024 Bahrain cohort reported a novel homozygous variant c.860A>G (p.Asp287Gly) in all eight patients in that cohort. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6)
• A 2021 case-series identified six novel variants (including splice and truncating) among eight patients. (wang2021cinicalmetabolicand pages 1-2)
• A 2015 sibling pair study reported a novel homozygous variant c.196C>T (p.Arg66Trp) and demonstrated enzyme deficiency in fibroblasts. (stiles2015successfuldiagnosisof pages 1-3, stiles2015successfuldiagnosisof pages 3-5)
• A 2023 case report described two novel variants c.782T>C (p.Leu261Pro) and c.1012-1G>A (compound heterozygous), with long-term MRI follow-up showing progressive cerebellar atrophy. (taura2023leighlikesyndromewith pages 1-2)

Variant class patterns (from cited case series): missense, truncating, and splice-site variants are all represented. (wang2021cinicalmetabolicand pages 1-2, taura2023leighlikesyndromewith pages 1-2)

4.3 Genotype–phenotype correlations (evidence-supported)

A multi-center aggregation reported survival differences suggesting genotype–outcome correlation: - Within HIBCH deficiency, homozygous variants inside/near the catalytic region were associated with worse survival than surface variants (log-rank P = 0.004). (marti‐sanchez2021delineatingtheneurological pages 3-5)

4.4 Modifier genes / epigenetics / chromosomal abnormalities

No modifier genes, epigenetic signatures, or recurrent chromosomal abnormalities were identified in the retrieved evidence. (evidence gap)

5. Environmental information

No specific toxins, lifestyle factors, or infectious agents were identified as causal; however, febrile illness/infection can act as a trigger for acute decompensation. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6, taura2023leighlikesyndromewith pages 1-2)

6. Mechanism / pathophysiology

6.1 Current mechanistic understanding

The working model is that loss of HIBCH activity perturbs valine degradation, with accumulation of upstream metabolites and reactive intermediates, contributing to mitochondrial dysfunction and Leigh/Leigh-like neurodegeneration. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, stiles2015successfuldiagnosisof pages 6-8)

A comparative clinical series emphasizes that HIBCH deficiency’s main biochemical hallmark is “Elevated plasma levels of 3-hydroxyisobutyryl carnitine” (marti‐sanchez2021delineatingtheneurological pages 7-8), and a diagnostic review for Leigh syndrome spectrum notes that in HIBCH deficiency acylcarnitine profiles may show “high levels of 3-hydroxyisobutyryl carnitine.” (baldo2024acomprehensiveapproach pages 2-4)

6.2 Causal chain (clinically oriented)

1. Biallelic HIBCH variants → reduced HIBCH activity in mitochondria (stiles2015successfuldiagnosisof pages 1-3, stiles2015successfuldiagnosisof pages 3-5)
2. Impaired valine catabolism → accumulation of valine-pathway metabolites (C4‑OH acylcarnitine; urine organic acids and cysteine/cysteamine conjugates) (wang2021cinicalmetabolicand pages 1-2, kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3)
3. Secondary mitochondrial dysfunction / energy failure in vulnerable tissues → Leigh/Leigh-like basal ganglia injury (stiles2015successfuldiagnosisof pages 1-3, marti‐sanchez2021delineatingtheneurological pages 7-8)
4. Clinical manifestations: neurodevelopmental regression, encephalopathy, movement disorders, seizures, feeding difficulties; potentially progressive neurodegeneration (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, marti‐sanchez2021delineatingtheneurological pages 7-8)

6.3 Pathways / ontology mapping suggestions

• GO biological process (suggestions): valine catabolic process; branched-chain amino acid catabolic process; mitochondrial metabolic process.
• GO cellular component (suggestions): mitochondrion / mitochondrial matrix.
• Cell Ontology (CL) suggestions for susceptible cell types: neurons (CL:0000540), astrocytes (CL:0000127), oligodendrocytes (CL:0000128), microglia (CL:0000129) (suggested because disease is primarily neurodegenerative with basal ganglia/cerebellar involvement).

(These ontology suggestions are provided for knowledge base structuring; the specific GO/CL identifiers were not enumerated in the retrieved full-text evidence.)

6.4 Molecular profiling / multi-omics

No transcriptomic/proteomic/metabolomic multi-omics studies specific to HIBCH deficiency were identified in the retrieved evidence set for this run beyond targeted metabolite profiling used diagnostically. (wang2021cinicalmetabolicand pages 1-2)

7. Anatomical structures affected

7.1 Organ-level involvement

The central nervous system is the primary affected system, with imaging lesions in the basal ganglia and sometimes cerebellar atrophy. (marti‐sanchez2021delineatingtheneurological pages 7-8, taura2023leighlikesyndromewith pages 1-2)

UBERON suggestions: - Basal ganglion UBERON:0002420 (suggested) - Globus pallidus UBERON:0001885 (suggested) - Cerebellum UBERON:0002037 (suggested)

7.2 Subcellular localization

Function is mitochondrial; the disease is framed in mitochondrial metabolism and mitochondrial disease diagnostics. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, baldo2024acomprehensiveapproach pages 2-4)

8. Temporal development

• Typical onset: infancy/early childhood, sometimes within the first months of life. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6, wang2021cinicalmetabolicand pages 1-2)
• Course patterns: progressive neurodegeneration and/or episodic decompensation; some patients show chronic phases after an acute encephalopathic event. (taura2023leighlikesyndromewith pages 1-2)

9. Inheritance and population

9.1 Inheritance

Autosomal recessive inheritance is supported by multiple pedigrees and case series (biallelic variants; heterozygous parents). (stiles2015successfuldiagnosisof pages 3-5, kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3)

9.2 Epidemiology / frequency estimates

• A 2024 Bahrain cohort cites OMIM-based estimates: “1 in 127,939 in East Asians” and “1 in 551,545 in Europeans.” (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3)
• Earlier series also cite incidence on the order of ~1 in 130,000 and suggest underdiagnosis. (stiles2015successfuldiagnosisof pages 1-3, wang2021cinicalmetabolicand pages 1-2)

9.3 Founder effects / population-specific variants

A Bahrain cohort reported a shared homozygous variant in all eight patients (consistent with a local recurrent variant), but also notes no broadly “confirmed founder mutation” across populations. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6)

Carrier frequency estimates from population databases were referenced in the 2015 study’s incidence modeling approach, but detailed per-population carrier frequencies were not present in the retrieved excerpts. (stiles2015successfuldiagnosisof pages 3-5)

10. Diagnostics

10.1 Clinical suspicion

HIBCH deficiency should be considered in infants/children with Leigh/Leigh-like presentation (basal ganglia lesions) and compatible metabolic findings, particularly when valine-pathway metabolites are present. (stiles2015successfuldiagnosisof pages 1-3, kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 1-2)

10.2 Biochemical testing (key real-world implementation)

Common diagnostic markers: - C4‑OH (hydroxy‑C4) acylcarnitine in dried blood spots or plasma; one series reported hydroxy‑C4 elevations on newborn screening cards, supporting NBS detectability when hydroxy‑C4 is measured. (stiles2015successfuldiagnosisof pages 5-6) - Urine metabolites used for screening/confirmation include S-(2-carboxypropyl) cysteine and S-(2-carboxypropyl) cysteamine and their carnitine esters (tandem MS), plus other valine-pathway organic acids. (kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3) - In a longitudinal case-series, urinary 2,3-dihydroxy-2-methylbutyrate was elevated in 6/7 and S-(2-carboxypropyl) cysteamine in 3/3, and dried blood spot C4‑OH elevation occurred in 5/7. (wang2021cinicalmetabolicand pages 1-2)

Important limitation: hydroxy‑C4 can be normal in milder phenotypes; thus reliance on a single marker may miss cases. (kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 1-2)

10.3 Genetic testing

NGS-based testing (gene panels, WES, WGS) is a primary route to diagnosis in modern practice; several reports demonstrate WES/WGS leading to diagnosis, including in Leigh-like presentations with variable/negative metabolic screens. (stiles2015successfuldiagnosisof pages 1-3, taura2023leighlikesyndromewith pages 1-2)

10.4 Enzymatic confirmation

Measurement of HIBCH activity in patient fibroblasts/tissues can confirm diagnosis but may not be widely available in routine clinical settings. (stiles2015successfuldiagnosisof pages 1-3, stiles2015successfuldiagnosisof pages 5-6)

10.5 Leigh syndrome spectrum diagnostic frameworks (2023–2024 development)

A 2024 diagnostic framework for Leigh syndrome spectrum recommends parallel biochemical testing and states: “basic metabolic studies are mandatory for all patients, including an L/P ratio, plasma amino acids and acylcarnitine profiles, and urinary organic acids” and that their approach “characterized 80% of our cohort and promoted specific intervention in 10% of confirmed cases.” (baldo2024acomprehensiveapproach pages 2-4, baldo2024acomprehensiveapproach pages 1-2)

10.6 Newborn screening status

• Retrospective analysis of newborn screening cards in an affected sibling pair showed elevated hydroxy‑C4 and suggested that “this disorder could be screened for by NBS programs” when hydroxy‑C4 is included/assessed. (stiles2015successfuldiagnosisof pages 1-3, stiles2015successfuldiagnosisof pages 5-6)
• No evidence was retrieved in this run that HIBCH deficiency is universally included in national newborn screening panels; implementation appears program-dependent. (evidence gap)

10.7 Differential diagnosis (examples)

Valine-pathway and related mitochondrial/Leigh-like conditions (e.g., ECHS1/SCEH deficiency) are prominent differentials; comparative neuroradiology and metabolite patterns (e.g., predominance of globus pallidus involvement; 3-hydroxyisobutyrylcarnitine) can help differentiate. (marti‐sanchez2021delineatingtheneurological pages 7-8, marti‐sanchez2021delineatingtheneurological pages 11-13)

11. Outcomes / prognosis

• Outcomes vary but can be severe. In the Bahrain cohort, despite interventions, many patients had severe persistent developmental delay and some died due to complications (including sepsis). (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3)
• Comparative survival analysis across aggregated cohorts found longer survival in HIBCH compared with ECHS1/SCEH deficiency (Breslow test P = 0.036). (marti‐sanchez2021delineatingtheneurological pages 3-5)
• Earlier onset was associated with poorer prognosis in the Bahrain cohort. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6)

12. Treatment

12.1 Current standard of care (no disease-specific curative therapy)

Management is largely supportive and empiric metabolic therapy: - Dietary management: low-valine / low-protein dietary strategies are commonly suggested/used. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3) - Supplements/adjuncts: carnitine and N-acetylcysteine are commonly mentioned; “mitochondrial cocktail” approaches are described in Leigh-like care contexts. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3) - Acute decompensation care: supportive management (e.g., IV fluids, correction of acidosis, high-glucose support) is described in severe presentations. (kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3)

Evidence for benefit is limited and mainly observational: - In an 8-patient longitudinal case-series, five patients “responded positively to treatment with a significant decrease in NPMDS scores” after drug and dietary treatment. (wang2021cinicalmetabolicand pages 1-2) - In a comparative cohort, only one patient showed “a mild improvement in lower limb dystonia while receiving valine restricted formula,” and most had no clear neurologic improvement. (marti‐sanchez2021delineatingtheneurological pages 7-8)

12.2 MAXO suggestions (treatment action ontology)

• Dietary amino acid restriction / valine restriction (MAXO term suggestion)
• Carnitine supplementation (MAXO term suggestion)
• N-acetylcysteine therapy (MAXO term suggestion)
• Supportive critical care for metabolic decompensation (MAXO term suggestion)

12.3 Clinical trials

No interventional clinical trials were identified in the tool-run state for HIBCH deficiency. (evidence gap)

13. Prevention

• Primary prevention: not applicable in the usual sense for an autosomal recessive Mendelian disorder, except via reproductive options.
• Secondary prevention: early detection (potentially via newborn screening hydroxy‑C4 signal where implemented) and early metabolic management may reduce decompensation risk, but controlled evidence is lacking. (stiles2015successfuldiagnosisof pages 1-3, stiles2015successfuldiagnosisof pages 5-6)
• Genetic counseling: indicated for affected families (implied by autosomal recessive inheritance and pedigree studies). (stiles2015successfuldiagnosisof pages 3-5, kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3)

14. Other species / natural disease

No naturally occurring veterinary HIBCH deficiency reports were identified in the retrieved evidence set. (evidence gap)

15. Model organisms

No HIBCH-deficiency-specific animal models or iPSC models were identified in the retrieved evidence for this run. (evidence gap)

Structured evidence table (for knowledge base entry)

Table: This table summarizes the main identifiers, pathway context, biomarkers, imaging features, onset pattern, and key references for 3-hydroxyisobutyryl-CoA hydrolase deficiency. It is designed as a compact evidence-backed reference for a disease knowledge base entry.

Recent developments and authoritative perspectives (2023–2024 emphasis)

• 2024 (Diagnostics, MDPI): A Leigh syndrome spectrum diagnostic pipeline emphasizes parallel metabolic testing and notes that biochemical workups can rapidly characterize cases and enable intervention in a subset, relevant because HIBCH deficiency is among treatable metabolic causes within LSS. URL: https://doi.org/10.3390/diagnostics14192133 (published Sep 2024). (baldo2024acomprehensiveapproach pages 2-4, baldo2024acomprehensiveapproach pages 1-2)
• 2024 (Bahrain cohort): A retrospective cohort expands real-world phenotypic and biochemical characterization, reports a recurrent homozygous variant in their population, and provides incidence estimates (from OMIM) across ancestries. URL: https://doi.org/10.24911/jbcgenetics.183-1722167696 (published Dec 2024). (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3, jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6)
• 2023 (Human Genome Variation): Long-term MRI follow-up in a genetically confirmed case expands the neuroradiologic spectrum to include progressive cerebellar atrophy. URL: https://doi.org/10.1038/s41439-023-00251-y (published Aug 2023). (taura2023leighlikesyndromewith pages 1-2)

Key direct abstract quotes (verbatim as captured in retrieved full text)

• Leigh syndrome spectrum diagnostics: “basic metabolic studies are mandatory for all patients, including an L/P ratio, plasma amino acids and acylcarnitine profiles, and urinary organic acids” (baldo2024acomprehensiveapproach pages 2-4).
• Leigh syndrome spectrum diagnostics: “characterized 80% of our cohort and promoted specific intervention in 10% of confirmed cases” (baldo2024acomprehensiveapproach pages 1-2).
• HIBCH vs ECHS1 comparative series (biochemistry): “Elevated plasma levels of 3-hydroxyisobutyryl carnitine” (marti‐sanchez2021delineatingtheneurological pages 7-8).
• HIBCH function statement: “HIBCH catalyses the fifth step of valine catabolism” (marti‐sanchez2021delineatingtheneurological pages 3-5).

Notable evidence gaps for knowledge base completion

1. MONDO, Orphanet, MeSH, and ICD mappings were not retrieved in the current tool-run evidence and require direct ontology/database queries.
2. Robust prevalence and carrier frequency statistics remain sparse; estimates exist but vary by ancestry and methodology.
3. No controlled clinical trials; treatment evidence is largely observational.
4. Limited published mechanistic studies directly in patient tissues; multi-omics and model organism resources appear underdeveloped in the retrieved set.

References

1. (stiles2015successfuldiagnosisof pages 1-3): Ashlee R. Stiles, Sacha Ferdinandusse, Arnaud Besse, Vivek Appadurai, Karen B. Leydiker, E.J. Cambray-Forker, Penelope E. Bonnen, and Jose E. Abdenur. Successful diagnosis of hibch deficiency from exome sequencing and positive retrospective analysis of newborn screening cards in two siblings presenting with leigh's disease. Molecular Genetics and Metabolism, 115(4):161-167, Aug 2015. URL: https://doi.org/10.1016/j.ymgme.2015.05.008, doi:10.1016/j.ymgme.2015.05.008. This article has 48 citations and is from a peer-reviewed journal.

2. (wang2021cinicalmetabolicand pages 1-2): Junling Wang, Zhimei Liu, Manting Xu, Xiaodi Han, Changhong Ren, Xinying Yang, Chunhua Zhang, and Fang Fang. Cinical, metabolic, and genetic analysis and follow-up of eight patients with hibch mutations presenting with leigh/leigh-like syndrome. Frontiers in Pharmacology, Mar 2021. URL: https://doi.org/10.3389/fphar.2021.605803, doi:10.3389/fphar.2021.605803. This article has 23 citations.

3. (marti‐sanchez2021delineatingtheneurological pages 7-8): Laura Marti‐Sanchez, Heidy Baide‐Mairena, Anna Marcé‐Grau, Roser Pons, Anastasia Skouma, Eduardo López‐Laso, Maria Sigatullina, Cristiano Rizzo, Michela Semeraro, Diego Martinelli, Rosalba Carrozzo, Carlo Dionisi‐Vici, Luis González‐Gutiérrez‐Solana, Marta Correa‐Vela, Juan Dario Ortigoza‐Escobar, Ángel Sánchez‐Montañez, Élida Vazquez, Ignacio Delgado, Sergio Aguilera‐Albesa, María Eugenia Yoldi, Antonia Ribes, Frederic Tort, Luca Pollini, Serena Galosi, Vincenzo Leuzzi, Manuela Tolve, Laura Pérez‐Gay, Luis Aldamiz‐Echevarría, Mireia Del Toro, Antonio Arranz, Filip Roelens, Roser Urreizti, Rafael Artuch, Alfons Macaya, and Belén Pérez‐Dueñas. Delineating the neurological phenotype in children with defects in the echs1 or hibch gene. Aug 2021. URL: https://doi.org/10.1002/jimd.12288, doi:10.1002/jimd.12288. This article has 48 citations and is from a peer-reviewed journal.

4. (baldo2024acomprehensiveapproach pages 2-4): Manuela Schubert Baldo, Luísa Azevedo, Margarida Paiva Coelho, Esmeralda Martins, and Laura Vilarinho. A comprehensive approach to the diagnosis of leigh syndrome spectrum. Diagnostics, 14:2133, Sep 2024. URL: https://doi.org/10.3390/diagnostics14192133, doi:10.3390/diagnostics14192133. This article has 2 citations.

5. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 1-3): Emtithal Al jishi, Zahra Al sahlawi, Huda Omran, Mohammed S. Almaliki, Faten Al mahroos, and Heba Alkoheji. Characterization of 3-hydroxyisobutyryl-coa hydrolase (hibch) deficiency in bahrain: a retrospective cohort study. Journal of Biochemical and Clinical Genetics, 7:068-074, Dec 2024. URL: https://doi.org/10.24911/jbcgenetics.183-1722167696, doi:10.24911/jbcgenetics.183-1722167696. This article has 0 citations.

6. (kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 1-2): Mustafa Kılıç and Fatma Kurt-Çolak. 3-hydroxyisobutyryl-coa hydrolase deficiency in a turkish child with a novel hibch gene mutation and literature review. Molecular Syndromology, 11:170-175, Jun 2020. URL: https://doi.org/10.1159/000508728, doi:10.1159/000508728. This article has 2 citations and is from a peer-reviewed journal.

7. (alayed2020metabolicacidosisand pages 2-3): Alaa M Alayed, Eissa Ali Faqeih, Abdulwahed Aldehaimi, Roy W A Peake, and and Naif A M Almontashiri. Metabolic acidosis and hypoglycemia in a child with leigh-like phenotype. Clinical chemistry, 66 5:739-741, May 2020. URL: https://doi.org/10.1093/clinchem/hvaa079, doi:10.1093/clinchem/hvaa079. This article has 1 citations and is from a highest quality peer-reviewed journal.

8. (taura2023leighlikesyndromewith pages 1-2): Yoshihiro Taura, Takenori Tozawa, Kenichi Isoda, Satori Hirai, Tomohiro Chiyonobu, Naoko Yano, Takahiro Hayashi, Takeshi Yoshida, and Tomoko Iehara. Leigh-like syndrome with progressive cerebellar atrophy caused by novel hibch variants. Human Genome Variation, Aug 2023. URL: https://doi.org/10.1038/s41439-023-00251-y, doi:10.1038/s41439-023-00251-y. This article has 8 citations.

9. (marti‐sanchez2021delineatingtheneurological pages 3-5): Laura Marti‐Sanchez, Heidy Baide‐Mairena, Anna Marcé‐Grau, Roser Pons, Anastasia Skouma, Eduardo López‐Laso, Maria Sigatullina, Cristiano Rizzo, Michela Semeraro, Diego Martinelli, Rosalba Carrozzo, Carlo Dionisi‐Vici, Luis González‐Gutiérrez‐Solana, Marta Correa‐Vela, Juan Dario Ortigoza‐Escobar, Ángel Sánchez‐Montañez, Élida Vazquez, Ignacio Delgado, Sergio Aguilera‐Albesa, María Eugenia Yoldi, Antonia Ribes, Frederic Tort, Luca Pollini, Serena Galosi, Vincenzo Leuzzi, Manuela Tolve, Laura Pérez‐Gay, Luis Aldamiz‐Echevarría, Mireia Del Toro, Antonio Arranz, Filip Roelens, Roser Urreizti, Rafael Artuch, Alfons Macaya, and Belén Pérez‐Dueñas. Delineating the neurological phenotype in children with defects in the echs1 or hibch gene. Aug 2021. URL: https://doi.org/10.1002/jimd.12288, doi:10.1002/jimd.12288. This article has 48 citations and is from a peer-reviewed journal.

10. (stiles2015successfuldiagnosisof pages 6-8): Ashlee R. Stiles, Sacha Ferdinandusse, Arnaud Besse, Vivek Appadurai, Karen B. Leydiker, E.J. Cambray-Forker, Penelope E. Bonnen, and Jose E. Abdenur. Successful diagnosis of hibch deficiency from exome sequencing and positive retrospective analysis of newborn screening cards in two siblings presenting with leigh's disease. Molecular Genetics and Metabolism, 115(4):161-167, Aug 2015. URL: https://doi.org/10.1016/j.ymgme.2015.05.008, doi:10.1016/j.ymgme.2015.05.008. This article has 48 citations and is from a peer-reviewed journal.

11. (stiles2015successfuldiagnosisof pages 3-5): Ashlee R. Stiles, Sacha Ferdinandusse, Arnaud Besse, Vivek Appadurai, Karen B. Leydiker, E.J. Cambray-Forker, Penelope E. Bonnen, and Jose E. Abdenur. Successful diagnosis of hibch deficiency from exome sequencing and positive retrospective analysis of newborn screening cards in two siblings presenting with leigh's disease. Molecular Genetics and Metabolism, 115(4):161-167, Aug 2015. URL: https://doi.org/10.1016/j.ymgme.2015.05.008, doi:10.1016/j.ymgme.2015.05.008. This article has 48 citations and is from a peer-reviewed journal.

12. (kılıc20203hydroxyisobutyrylcoahydrolasedeficiency pages 3-3): Mustafa Kılıç and Fatma Kurt-Çolak. 3-hydroxyisobutyryl-coa hydrolase deficiency in a turkish child with a novel hibch gene mutation and literature review. Molecular Syndromology, 11:170-175, Jun 2020. URL: https://doi.org/10.1159/000508728, doi:10.1159/000508728. This article has 2 citations and is from a peer-reviewed journal.

13. (jishi2024characterizationof3hydroxyisobutyrylcoa pages 4-6): Emtithal Al jishi, Zahra Al sahlawi, Huda Omran, Mohammed S. Almaliki, Faten Al mahroos, and Heba Alkoheji. Characterization of 3-hydroxyisobutyryl-coa hydrolase (hibch) deficiency in bahrain: a retrospective cohort study. Journal of Biochemical and Clinical Genetics, 7:068-074, Dec 2024. URL: https://doi.org/10.24911/jbcgenetics.183-1722167696, doi:10.24911/jbcgenetics.183-1722167696. This article has 0 citations.

14. (taura2023leighlikesyndromewith pages 2-3): Yoshihiro Taura, Takenori Tozawa, Kenichi Isoda, Satori Hirai, Tomohiro Chiyonobu, Naoko Yano, Takahiro Hayashi, Takeshi Yoshida, and Tomoko Iehara. Leigh-like syndrome with progressive cerebellar atrophy caused by novel hibch variants. Human Genome Variation, Aug 2023. URL: https://doi.org/10.1038/s41439-023-00251-y, doi:10.1038/s41439-023-00251-y. This article has 8 citations.

15. (stiles2015successfuldiagnosisof pages 5-6): Ashlee R. Stiles, Sacha Ferdinandusse, Arnaud Besse, Vivek Appadurai, Karen B. Leydiker, E.J. Cambray-Forker, Penelope E. Bonnen, and Jose E. Abdenur. Successful diagnosis of hibch deficiency from exome sequencing and positive retrospective analysis of newborn screening cards in two siblings presenting with leigh's disease. Molecular Genetics and Metabolism, 115(4):161-167, Aug 2015. URL: https://doi.org/10.1016/j.ymgme.2015.05.008, doi:10.1016/j.ymgme.2015.05.008. This article has 48 citations and is from a peer-reviewed journal.

16. (baldo2024acomprehensiveapproach pages 1-2): Manuela Schubert Baldo, Luísa Azevedo, Margarida Paiva Coelho, Esmeralda Martins, and Laura Vilarinho. A comprehensive approach to the diagnosis of leigh syndrome spectrum. Diagnostics, 14:2133, Sep 2024. URL: https://doi.org/10.3390/diagnostics14192133, doi:10.3390/diagnostics14192133. This article has 2 citations.

17. (marti‐sanchez2021delineatingtheneurological pages 11-13): Laura Marti‐Sanchez, Heidy Baide‐Mairena, Anna Marcé‐Grau, Roser Pons, Anastasia Skouma, Eduardo López‐Laso, Maria Sigatullina, Cristiano Rizzo, Michela Semeraro, Diego Martinelli, Rosalba Carrozzo, Carlo Dionisi‐Vici, Luis González‐Gutiérrez‐Solana, Marta Correa‐Vela, Juan Dario Ortigoza‐Escobar, Ángel Sánchez‐Montañez, Élida Vazquez, Ignacio Delgado, Sergio Aguilera‐Albesa, María Eugenia Yoldi, Antonia Ribes, Frederic Tort, Luca Pollini, Serena Galosi, Vincenzo Leuzzi, Manuela Tolve, Laura Pérez‐Gay, Luis Aldamiz‐Echevarría, Mireia Del Toro, Antonio Arranz, Filip Roelens, Roser Urreizti, Rafael Artuch, Alfons Macaya, and Belén Pérez‐Dueñas. Delineating the neurological phenotype in children with defects in the echs1 or hibch gene. Aug 2021. URL: https://doi.org/10.1002/jimd.12288, doi:10.1002/jimd.12288. This article has 48 citations and is from a peer-reviewed journal.

18. (taura2023leighlikesyndromewith pages 3-4): Yoshihiro Taura, Takenori Tozawa, Kenichi Isoda, Satori Hirai, Tomohiro Chiyonobu, Naoko Yano, Takahiro Hayashi, Takeshi Yoshida, and Tomoko Iehara. Leigh-like syndrome with progressive cerebellar atrophy caused by novel hibch variants. Human Genome Variation, Aug 2023. URL: https://doi.org/10.1038/s41439-023-00251-y, doi:10.1038/s41439-023-00251-y. This article has 8 citations.

19. (marti‐sanchez2021delineatingtheneurological pages 1-3): Laura Marti‐Sanchez, Heidy Baide‐Mairena, Anna Marcé‐Grau, Roser Pons, Anastasia Skouma, Eduardo López‐Laso, Maria Sigatullina, Cristiano Rizzo, Michela Semeraro, Diego Martinelli, Rosalba Carrozzo, Carlo Dionisi‐Vici, Luis González‐Gutiérrez‐Solana, Marta Correa‐Vela, Juan Dario Ortigoza‐Escobar, Ángel Sánchez‐Montañez, Élida Vazquez, Ignacio Delgado, Sergio Aguilera‐Albesa, María Eugenia Yoldi, Antonia Ribes, Frederic Tort, Luca Pollini, Serena Galosi, Vincenzo Leuzzi, Manuela Tolve, Laura Pérez‐Gay, Luis Aldamiz‐Echevarría, Mireia Del Toro, Antonio Arranz, Filip Roelens, Roser Urreizti, Rafael Artuch, Alfons Macaya, and Belén Pérez‐Dueñas. Delineating the neurological phenotype in children with defects in the echs1 or hibch gene. Aug 2021. URL: https://doi.org/10.1002/jimd.12288, doi:10.1002/jimd.12288. This article has 48 citations and is from a peer-reviewed journal.

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